TY  - JOUR
TI  - Artificial Intelligence Approach for Modeling and Forecasting Oil-Price Volatility
AU  - Al-Fattah, Saud M.
T2  - SPE Reservoir Evaluation & Engineering
AB  - Summary
Oil market volatility affects macroeconomic conditions and can unduly affect the economies of oil-producing countries. Large price swings can be detrimental to producers and consumers, causing infrastructure and capacity investments to be delayed, employment losses, inefficient investments, and/or the growth potential for energy-producing countries to be adversely affected. Undoubtedly, greater stability of oil prices increases the certainty of oil markets for the benefit of oil consumers and producers. Therefore, modeling and forecasting crude-oil price volatility is a strategic endeavor for many oil market and investment applications.
This paper focuses on the development of a new predictive model for describing and forecasting the behavior and dynamics of global oil-price volatility. Using a hybrid approach of artificial intelligence with a genetic algorithm (GA), artificial neural network (ANN), and data mining (DM) time-series (TS), a (GANNATS) model was developed to forecast the futures price volatility of West Texas Intermediate (WTI) crude. The WTI price volatility model was successfully designed, trained, verified, and tested using historical oil market data. The predictions from the GANNATS model closely matched the historical data of WTI futures price volatility. The model not only described the behavior and captured the dynamics of oil-price volatility, but also demonstrated the capability for predicting the direction of movements of oil market volatility with an accuracy of 88%.
The model is applicable as a predictive tool for oil-price volatility and its direction of movements, benefiting oil producers, consumers, investors, and traders. It assists these key market players in making sound decisions and taking corrective courses of action for oil market stability, development strategies, and future investments; this could lead to increased profits and to reduced costs and market losses. In addition, this improved method for modeling oil-price volatility enables experts and market analysts to empirically test new approaches for mitigating market volatility. It also provides a roadmap for improving the predictability and accuracy of energy and crude models.
DA  - 2019/08/15/
PY  - 2019
DO  - 10.2118/195584-PA
DP  - DOI.org (Crossref)
VL  - 22
IS  - 03
SP  - 817
EP  - 826
LA  - en
SN  - 1094-6470, 1930-0212
UR  - https://onepetro.org/REE/article/22/03/817/207108/Artificial-Intelligence-Approach-for-Modeling-and
Y2  - 2021/12/06/19:40:06
ER  - 

TY  - JOUR
TI  - A hybrid option pricing model using a neural network for estimating volatility
AU  - Amornwattana, Sunisa
AU  - Enke, David
AU  - Dagli, Cihan H.
T2  - International Journal of General Systems
DA  - 2007/10//
PY  - 2007
DO  - 10.1080/03081070701210303
DP  - DOI.org (Crossref)
VL  - 36
IS  - 5
SP  - 558
EP  - 573
J2  - International Journal of General Systems
LA  - en
SN  - 0308-1079, 1563-5104
UR  - http://www.tandfonline.com/doi/abs/10.1080/03081070701210303
Y2  - 2021/12/06/19:45:02
L4  - http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=01496BFBC89E0645A7D83E1C4B920CF3?doi=10.1.1.578.5116&rep=rep1&type=pdf
ER  - 

TY  - JOUR
TI  - bibliometrix : An R-tool for comprehensive science mapping analysis
AU  - Aria, Massimo
AU  - Cuccurullo, Corrado
T2  - Journal of Informetrics
DA  - 2017/11//
PY  - 2017
DO  - 10.1016/j.joi.2017.08.007
DP  - DOI.org (Crossref)
VL  - 11
IS  - 4
SP  - 959
EP  - 975
J2  - Journal of Informetrics
LA  - en
SN  - 17511577
ST  - bibliometrix
UR  - https://linkinghub.elsevier.com/retrieve/pii/S1751157717300500
Y2  - 2021/12/06/19:45:38
ER  - 

TY  - JOUR
TI  - Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods
AU  - Arvin, Ramin
AU  - Khattak, Asad J.
AU  - Qi, Hairong
T2  - Accident Analysis & Prevention
DA  - 2021/03//
PY  - 2021
DO  - 10.1016/j.aap.2020.105949
DP  - DOI.org (Crossref)
VL  - 151
SP  - 105949
J2  - Accident Analysis & Prevention
LA  - en
SN  - 00014575
ST  - Safety critical event prediction through unified analysis of driver and vehicle volatilities
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0001457520317693
Y2  - 2021/12/07/02:34:04
ER  - 

TY  - JOUR
TI  - A hybrid artificial neural network-GJR modeling approach to forecasting currency exchange rate volatility
AU  - Amo Baffour, Alexander
AU  - Feng, Jingchun
AU  - Taylor, Evans Kwesi
T2  - Neurocomputing
DA  - 2019/11//
PY  - 2019
DO  - 10.1016/j.neucom.2019.07.088
DP  - DOI.org (Crossref)
VL  - 365
SP  - 285
EP  - 301
J2  - Neurocomputing
LA  - en
SN  - 09252312
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0925231219310951
Y2  - 2021/12/07/02:36:40
ER  - 

TY  - JOUR
TI  - Direction-of-change forecasting using a volatility-based recurrent neural network
AU  - Bekiros, S. D.
AU  - Georgoutsos, D. A.
T2  - Journal of Forecasting
DA  - 2008/08//
PY  - 2008
DO  - 10.1002/for.1063
DP  - DOI.org (Crossref)
VL  - 27
IS  - 5
SP  - 407
EP  - 417
J2  - J. Forecast.
LA  - en
SN  - 02776693, 1099131X
UR  - https://onlinelibrary.wiley.com/doi/10.1002/for.1063
Y2  - 2021/12/07/02:37:58
ER  - 

TY  - JOUR
TI  - Forecasting volatility in oil prices with a class of nonlinear volatility models: smooth transition RBF and MLP neural networks augmented GARCH approach
AU  - Bildirici, Melike
AU  - Ersin, Özgür
T2  - Petroleum Science
DA  - 2015/08//
PY  - 2015
DO  - 10.1007/s12182-015-0035-8
DP  - DOI.org (Crossref)
VL  - 12
IS  - 3
SP  - 534
EP  - 552
J2  - Pet. Sci.
LA  - en
SN  - 1672-5107, 1995-8226
ST  - Forecasting volatility in oil prices with a class of nonlinear volatility models
UR  - http://link.springer.com/10.1007/s12182-015-0035-8
Y2  - 2021/12/07/02:41:05
L4  - https://link.springer.com/content/pdf/10.1007%2Fs12182-015-0035-8.pdf
ER  - 

TY  - JOUR
TI  - Realized Volatility Forecasting with Neural Networks
AU  - Bucci, Andrea
T2  - Journal of Financial Econometrics
AB  - Abstract
In the last few decades, a broad strand of literature in finance has implemented artificial neural networks as a forecasting method. The major advantage of this approach is the possibility to approximate any linear and nonlinear behaviors without knowing the structure of the data generating process. This makes it suitable for forecasting time series which exhibit long-memory and nonlinear dependencies, like conditional volatility. In this article, the predictive performance of feed-forward and recurrent neural networks (RNNs) was compared, particularly focusing on the recently developed long short-term memory (LSTM) network and nonlinear autoregressive model process with eXogenous input (NARX) network, with traditional econometric approaches. The results show that RNNs are able to outperform all the traditional econometric methods. Additionally, capturing long-range dependence through LSTM and NARX models seems to improve the forecasting accuracy also in a highly volatile period.
DA  - 2020/06/01/
PY  - 2020
DO  - 10.1093/jjfinec/nbaa008
DP  - DOI.org (Crossref)
VL  - 18
IS  - 3
SP  - 502
EP  - 531
LA  - en
SN  - 1479-8409, 1479-8417
UR  - https://academic.oup.com/jfec/article/18/3/502/5856840
Y2  - 2021/12/07/02:43:11
L4  - https://mpra.ub.uni-muenchen.de/95443/1/MPRA_paper_95443.pdf
ER  - 

TY  - JOUR
TI  - Forecasting large scale conditional volatility and covariance using neural network on GPU
AU  - Cai, Xianggao
AU  - Lai, Guoming
AU  - Lin, Xiaola
T2  - The Journal of Supercomputing
DA  - 2013/02//
PY  - 2013
DO  - 10.1007/s11227-012-0827-1
DP  - DOI.org (Crossref)
VL  - 63
IS  - 2
SP  - 490
EP  - 507
J2  - J Supercomput
LA  - en
SN  - 0920-8542, 1573-0484
UR  - http://link.springer.com/10.1007/s11227-012-0827-1
Y2  - 2021/12/07/02:44:52
ER  - 

TY  - JOUR
TI  - A neural network approach to understanding implied volatility movements
AU  - Cao, Jay
AU  - Chen, Jacky
AU  - Hull, John
T2  - Quantitative Finance
DA  - 2020/09/01/
PY  - 2020
DO  - 10.1080/14697688.2020.1750679
DP  - DOI.org (Crossref)
VL  - 20
IS  - 9
SP  - 1405
EP  - 1413
J2  - Quantitative Finance
LA  - en
SN  - 1469-7688, 1469-7696
UR  - https://www.tandfonline.com/doi/full/10.1080/14697688.2020.1750679
Y2  - 2021/12/07/02:50:08
ER  - 

TY  - JOUR
TI  - Forecasting volatility with support vector machine-based GARCH model
AU  - Chen, Shiyi
AU  - Härdle, Wolfgang K.
AU  - Jeong, Kiho
T2  - Journal of Forecasting
DA  - 2009///
PY  - 2009
DO  - 10.1002/for.1134
DP  - DOI.org (Crossref)
SP  - n/a
EP  - n/a
J2  - J. Forecast.
LA  - en
SN  - 02776693, 1099131X
UR  - https://onlinelibrary.wiley.com/doi/10.1002/for.1134
Y2  - 2021/12/07/02:51:40
ER  - 

TY  - JOUR
TI  - An artificial neural network augmented GARCH model for Islamic stock market volatility: Do asymmetry and long memory matter?
AU  - Chkili, Walid
AU  - Hamdi, Manel
T2  - International Journal of Islamic and Middle Eastern Finance and Management
AB  - Purpose
The purpose of this study is to investigate the volatility and forecast accuracy of the Islamic stock market for the period 1999–2017. This period is characterized by the occurrence of several economic and political events such as the September 11, 2001, terrorist attack and the 2007–2008 global financial crisis.


Design/methodology/approach
This study constructs a new hybrid generalized autoregressive conditional heteroskedasticity (GARCH)-type model based on an artificial neural network (ANN). This model is applied to the daily Dow Jones Islamic Market World Index during the period June 1999–January 2017.


Findings
The in-sample results show that the volatility of the Islamic stock market can be better described by the fractionally integrated asymmetric power ARCH (FIAPARCH) approach that takes into account asymmetry and long memory features. Considering the out-of-sample analysis, this paper has applied a hybrid forecasting model, which combines the FIAPARCH approach and the ANN. Empirical results reveal that the proposed hybrid model (FIAPARCH-ANN) outperforms all other single models such as GARCH, fractional integrated GARCH and FIAPARCH in terms of all performance criteria used in the study.


Practical implications
The results have some implications for Islamic investors, portfolio managers and policymakers. These implications are related to the optimal portfolio diversification decision, the hedging strategy choice and the risk management analysis.


Originality/value
The paper develops a new framework that combines an ANN and FIAPARCH model that introduces two important features of time series, namely, asymmetry and long memory.
DA  - 2021/11/04/
PY  - 2021
DO  - 10.1108/IMEFM-05-2019-0204
DP  - DOI.org (Crossref)
VL  - 14
IS  - 5
SP  - 853
EP  - 873
J2  - IMEFM
LA  - en
SN  - 1753-8394, 1753-8394
ST  - An artificial neural network augmented GARCH model for Islamic stock market volatility
UR  - https://www.emerald.com/insight/content/doi/10.1108/IMEFM-05-2019-0204/full/html
Y2  - 2021/12/07/02:52:30
ER  - 

TY  - CHAP
TI  - Engineering a Generalized Neural Network Mapping of Volatility Spillovers in European Government Bond Markets
AU  - Dash, Gordon H.
AU  - Kajiji, Nina
T2  - Handbook of Financial Engineering
A2  - Zopounidis, Constantin
A2  - Doumpos, Michael
A2  - Pardalos, Panos M.
CY  - Boston, MA
DA  - 2008///
PY  - 2008
DP  - DOI.org (Crossref)
VL  - 18
SP  - 201
EP  - 230
LA  - en
PB  - Springer US
SN  - 978-0-387-76681-2 978-0-387-76682-9
UR  - http://link.springer.com/10.1007/978-0-387-76682-9_7
Y2  - 2021/12/07/02:56:04
ER  - 

TY  - JOUR
TI  - Machine Learning in Finance: From Theory to Practice: by Matthew F. Dixon, Igor Halperin, and Paul Bilokon, Springer (2020). ISBN 978-3-030-41067-4. Paperback.
AU  - Coqueret, Guillaume
T2  - Quantitative Finance
DA  - 2021/01/02/
PY  - 2021
DO  - 10.1080/14697688.2020.1828609
DP  - DOI.org (Crossref)
VL  - 21
IS  - 1
SP  - 9
EP  - 10
J2  - Quantitative Finance
LA  - en
SN  - 1469-7688, 1469-7696
ST  - Machine Learning in Finance
UR  - https://www.tandfonline.com/doi/full/10.1080/14697688.2020.1828609
Y2  - 2021/12/07/02:56:53
ER  - 

TY  - JOUR
TI  - Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility
AU  - Ewees, Ahmed A.
AU  - Elaziz, Mohamed Abd
AU  - Alameer, Zakaria
AU  - Ye, Haiwang
AU  - Jianhua, Zhang
T2  - Resources Policy
DA  - 2020/03//
PY  - 2020
DO  - 10.1016/j.resourpol.2019.101555
DP  - DOI.org (Crossref)
VL  - 65
SP  - 101555
J2  - Resources Policy
LA  - en
SN  - 03014207
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0301420719300832
Y2  - 2021/12/07/02:59:18
ER  - 

TY  - JOUR
TI  - Pricing options with dual volatility input to modular neural networks
AU  - Fadda, Sadi
T2  - Borsa Istanbul Review
DA  - 2020/09//
PY  - 2020
DO  - 10.1016/j.bir.2020.03.002
DP  - DOI.org (Crossref)
VL  - 20
IS  - 3
SP  - 269
EP  - 278
J2  - Borsa Istanbul Review
LA  - en
SN  - 22148450
UR  - https://linkinghub.elsevier.com/retrieve/pii/S2214845020300168
Y2  - 2021/12/07/02:59:56
ER  - 

TY  - JOUR
TI  - A mine main fans switchover system with lower air flow volatility based on improved particle swarm optimization algorithm
AU  - Ge, Hengqing
AU  - Xu, Guang
AU  - Huang, Jinxin
AU  - Ma, Xiaoping
T2  - Advances in Mechanical Engineering
AB  - A reliable ventilation system is essential for maintaining a comfortable working environment and ensuring safety production in an underground coal mine. The automated fan switchover technique was developed for changing the main fan for maintenance with lower air flow volatility of underground mine in the switchover process. This article established the optimization model in the main fans switchover process, used the improved particle swarm optimization algorithm to solve the model, and achieved minimum air flow volatility in the fans switchover process. Compared to previous studies, computer simulations have shown that the proposed algorithm can effectively find the global optimal solution with less initial parameters and achieved lower air flow volatility in underground mine. The particle swarm optimization solution, searching diversity, prevents it from confining to local optimal solutions and enhances convergence. The reasonable step length is beneficial to reduce the air flow volatility and main fans switchover time. The air flow volatility is larger comparatively when some doors are nearly open or closed fully at the start–stop phase of the switchover process. A case application in a China’s domestic coal mine shows that the air flow volatility of the underground mine in the main fans switchover process is no more than 0.4%.
DA  - 2019/03//
PY  - 2019
DO  - 10.1177/1687814019829281
DP  - DOI.org (Crossref)
VL  - 11
IS  - 3
SP  - 168781401982928
J2  - Advances in Mechanical Engineering
LA  - en
SN  - 1687-8140, 1687-8140
UR  - http://journals.sagepub.com/doi/10.1177/1687814019829281
Y2  - 2021/12/07/03:06:51
L4  - https://journals.sagepub.com/doi/pdf/10.1177/1687814019829281
ER  - 

TY  - JOUR
TI  - Machine learning in energy economics and finance: A review
AU  - Ghoddusi, Hamed
AU  - Creamer, Germán G.
AU  - Rafizadeh, Nima
T2  - Energy Economics
DA  - 2019/06//
PY  - 2019
DO  - 10.1016/j.eneco.2019.05.006
DP  - DOI.org (Crossref)
VL  - 81
SP  - 709
EP  - 727
J2  - Energy Economics
LA  - en
SN  - 01409883
ST  - Machine learning in energy economics and finance
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0140988319301513
Y2  - 2021/12/07/03:07:39
ER  - 

TY  - JOUR
TI  - Forecasting stock volatility process using improved least square support vector machine approach
AU  - Gong, Xiao-Li
AU  - Liu, Xi-Hua
AU  - Xiong, Xiong
AU  - Zhuang, Xin-Tian
T2  - Soft Computing
DA  - 2019/11//
PY  - 2019
DO  - 10.1007/s00500-018-03743-0
DP  - DOI.org (Crossref)
VL  - 23
IS  - 22
SP  - 11867
EP  - 11881
J2  - Soft Comput
LA  - en
SN  - 1432-7643, 1433-7479
UR  - http://link.springer.com/10.1007/s00500-018-03743-0
Y2  - 2021/12/07/03:11:06
ER  - 

TY  - JOUR
TI  - Investor Confidence and Forecastability of US Stock Market Realized Volatility: Evidence from Machine Learning
AU  - Gupta, Rangan
AU  - Nel, Jacobus
AU  - Pierdzioch, Christian
T2  - Journal of Behavioral Finance
DA  - 2021/07/10/
PY  - 2021
DO  - 10.1080/15427560.2021.1949719
DP  - DOI.org (Crossref)
SP  - 1
EP  - 12
J2  - Journal of Behavioral Finance
LA  - en
SN  - 1542-7560, 1542-7579
ST  - Investor Confidence and Forecastability of US Stock Market Realized Volatility
UR  - https://www.tandfonline.com/doi/full/10.1080/15427560.2021.1949719
Y2  - 2021/12/07/03:12:31
L4  - https://www.up.ac.za/media/shared/61/WP/wp_2021_18.zp199890.pdf
ER  - 

TY  - JOUR
TI  - Using neural networks for forecasting volatility of S&P 500 Index futures prices
AU  - Hamid, Shaikh A.
AU  - Iqbal, Zahid
T2  - Journal of Business Research
DA  - 2004/10//
PY  - 2004
DO  - 10.1016/S0148-2963(03)00043-2
DP  - DOI.org (Crossref)
VL  - 57
IS  - 10
SP  - 1116
EP  - 1125
J2  - Journal of Business Research
LA  - en
SN  - 01482963
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0148296303000432
Y2  - 2021/12/07/03:14:16
ER  - 

TY  - JOUR
TI  - Employing machine learning techniques to assess requirement change volatility
AU  - Hein, Phyo Htet
AU  - Kames, Elisabeth
AU  - Chen, Cheng
AU  - Morkos, Beshoy
T2  - Research in Engineering Design
AB  - Abstract
Lack of planning when changing requirements to reflect stakeholders’ expectations can lead to propagated changes that can cause project failures. Existing tools cannot provide the formal reasoning required to manage requirement change and minimize unanticipated change propagation. This research explores machine learning techniques to predict requirement change volatility (RCV) using complex network metrics based on the premise that requirement networks can be utilized to study change propagation. Three research questions (RQs) are addressed: (1) Can RCV be measured through four classes namely, multiplier, absorber, transmitter, and robust, during every instance of change? (2) Can complex network metrics be explored and computed for each requirement during every instance of change? (3) Can machine learning techniques, specifically, multilabel learning (MLL) methods be employed to predict RCV using complex network metrics? RCV in this paper quantifies volatility for change propagation, that is, how requirements behave in response to the initial change. A multiplier is a requirement that is changed by an initial change and propagates change to other requirements. An absorber is a requirement that is changed by an initial change, but does not propagate change to other requirements. A transmitter is a requirement that is not changed by an initial change, but propagates change to other requirements. A robust requirement is a requirement that is not changed by an initial change and does not propagate change to other requirements. RCV is determined using industrial data and requirement network relationships obtained from previously developed Refined Automated Requirement Change Propagation Prediction (R-ARCPP) tool. Useful complex network metrics in highest performing machine learning models are discussed along with the limitations and future directions of this research.
DA  - 2021/04//
PY  - 2021
DO  - 10.1007/s00163-020-00353-6
DP  - DOI.org (Crossref)
VL  - 32
IS  - 2
SP  - 245
EP  - 269
J2  - Res Eng Design
LA  - en
SN  - 0934-9839, 1435-6066
UR  - http://link.springer.com/10.1007/s00163-020-00353-6
Y2  - 2021/12/07/03:15:17
L4  - https://link.springer.com/content/pdf/10.1007/s00163-020-00353-6.pdf
ER  - 

TY  - JOUR
TI  - Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models
AU  - Horvath, Blanka
AU  - Muguruza, Aitor
AU  - Tomas, Mehdi
T2  - Quantitative Finance
DA  - 2021/01/02/
PY  - 2021
DO  - 10.1080/14697688.2020.1817974
DP  - DOI.org (Crossref)
VL  - 21
IS  - 1
SP  - 11
EP  - 27
J2  - Quantitative Finance
LA  - en
SN  - 1469-7688, 1469-7696
ST  - Deep learning volatility
UR  - https://www.tandfonline.com/doi/full/10.1080/14697688.2020.1817974
Y2  - 2021/12/07/03:16:19
ER  - 

TY  - JOUR
TI  - A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction
AU  - Hu, Yan
AU  - Ni, Jian
AU  - Wen, Liu
T2  - Physica A: Statistical Mechanics and its Applications
DA  - 2020/11//
PY  - 2020
DO  - 10.1016/j.physa.2020.124907
DP  - DOI.org (Crossref)
VL  - 557
SP  - 124907
J2  - Physica A: Statistical Mechanics and its Applications
LA  - en
SN  - 03784371
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0378437120304696
Y2  - 2021/12/07/03:17:20
ER  - 

TY  - JOUR
TI  - Adaptive Fuzzy-GARCH model applied to forecasting the volatility of stock markets using particle swarm optimization
AU  - Hung, Jui-Chung
T2  - Information Sciences
DA  - 2011/10//
PY  - 2011
DO  - 10.1016/j.ins.2011.02.027
DP  - DOI.org (Crossref)
VL  - 181
IS  - 20
SP  - 4673
EP  - 4683
J2  - Information Sciences
LA  - en
SN  - 00200255
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0020025511001095
Y2  - 2021/12/07/03:18:11
ER  - 

TY  - JOUR
TI  - Robust Kalman filter based on a fuzzy GARCH model to forecast volatility using particle swarm optimization
AU  - Hung, Jui-Chung
T2  - Soft Computing
DA  - 2015/10//
PY  - 2015
DO  - 10.1007/s00500-014-1447-x
DP  - DOI.org (Crossref)
VL  - 19
IS  - 10
SP  - 2861
EP  - 2869
J2  - Soft Comput
LA  - en
SN  - 1432-7643, 1433-7479
UR  - http://link.springer.com/10.1007/s00500-014-1447-x
Y2  - 2021/12/07/03:19:09
ER  - 

TY  - JOUR
TI  - Classifying the Level of Bid Price Volatility Based on Machine Learning with Parameters from Bid Documents as Risk Factors
AU  - Jang, YeEun
AU  - Son, JeongWook
AU  - Yi, June-Seong
T2  - Sustainability
AB  - The purpose of this study is to classify the bid price volatility level with machine learning and parameters from bid documents as risk factors. To this end, we studied project-oriented risk factors affecting the bid price and pre-bid clarification document as the uncertainty of bid documents through preliminary research. The authors collected Caltrans’s bid summary and pre-bid clarification document from 2011–2018 as data samples. To train the classification model, the data were preprocessed to create a final dataset of 269 projects consisting of input and output parameters. The projects in which the bid inquiries were not resolved in the pre-bid clarification had higher bid averages and bid ranges than the risk-resolved projects. Besides this, regarding the two classification models with neural network (NN) algorithms, Model 2, which included the uncertainty in the bid documents as a parameter, predicted the bid average risk and bid range risk more accurately (52.5% and 72.5%, respectively) than Model 1 (26.4% and 23.3%, respectively). The accuracy of Model 2 was verified with 40 verification test datasets.
DA  - 2021/04/01/
PY  - 2021
DO  - 10.3390/su13073886
DP  - DOI.org (Crossref)
VL  - 13
IS  - 7
SP  - 3886
J2  - Sustainability
LA  - en
SN  - 2071-1050
UR  - https://www.mdpi.com/2071-1050/13/7/3886
Y2  - 2021/12/07/03:19:58
L4  - https://www.mdpi.com/2071-1050/13/7/3886/pdf
ER  - 

TY  - JOUR
TI  - European call price modelling using neural networks in considering volatility as stochastic with comparison to the Heston model
AU  - Jerbi, Yacin
AU  - Chaabene, Samira
T2  - Journal of Statistical Computation and Simulation
DA  - 2020/07/02/
PY  - 2020
DO  - 10.1080/00949655.2020.1747463
DP  - DOI.org (Crossref)
VL  - 90
IS  - 10
SP  - 1793
EP  - 1810
J2  - Journal of Statistical Computation and Simulation
LA  - en
SN  - 0094-9655, 1563-5163
UR  - https://www.tandfonline.com/doi/full/10.1080/00949655.2020.1747463
Y2  - 2021/12/07/03:21:12
ER  - 

TY  - JOUR
TI  - Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function
AU  - Jia, Fang
AU  - Yang, Boli
T2  - Complexity
A2  - Tabak, Benjamin Miranda
AB  - Volatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network (DNN) and long short-term memory (LSTM) model to forecast the volatility of stock index. Most related research studies use distance loss function to train the machine learning models, and they gain two disadvantages. The first one is that they introduce errors when using estimated volatility to be the forecasting target, and the second one is that their models cannot be compared to econometric models fairly. To solve these two problems, we further introduce a likelihood-based loss function to train the deep learning models and test all the models by the likelihood of the test sample. The results show that our deep learning models with likelihood-based loss function can forecast volatility more precisely than the econometric model and the deep learning models with distance loss function, and the LSTM model is the better one in the two deep learning models with likelihood-based loss function.
DA  - 2021/02/25/
PY  - 2021
DO  - 10.1155/2021/5511802
DP  - DOI.org (Crossref)
VL  - 2021
SP  - 1
EP  - 13
J2  - Complexity
LA  - en
SN  - 1099-0526, 1076-2787
ST  - Forecasting Volatility of Stock Index
UR  - https://www.hindawi.com/journals/complexity/2021/5511802/
Y2  - 2021/12/07/03:22:18
L4  - https://downloads.hindawi.com/journals/complexity/2021/5511802.pdf
ER  - 

TY  - JOUR
TI  - Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder-LSTM Techniques
AU  - Jung, Gunho
AU  - Choi, Sun-Yong
T2  - Complexity
A2  - Tabak, Benjamin Miranda
AB  - Since the breakdown of the Bretton Woods system in the early 1970s, the foreign exchange (FX) market has become an important focus of both academic and practical research. There are many reasons why FX is important, but one of most important aspects is the determination of foreign investment values. Therefore, FX serves as the backbone of international investments and global trading. Additionally, because fluctuations in FX affect the value of imported and exported goods and services, such fluctuations have an important impact on the economic competitiveness of multinational corporations and countries. Therefore, the volatility of FX rates is a major concern for scholars and practitioners. Forecasting FX volatility is a crucial financial problem that is attracting significant attention based on its diverse implications. Recently, various deep learning models based on artificial neural networks (ANNs) have been widely employed in finance and economics, particularly for forecasting volatility. The main goal of this study was to predict FX volatility effectively using ANN models. To this end, we propose a hybrid model that combines the long short-term memory (LSTM) and autoencoder models. These deep learning models are known to perform well in time-series prediction for forecasting FX volatility. Therefore, we expect that our approach will be suitable for FX volatility prediction because it combines the merits of these two models. Methodologically, we employ the Foreign Exchange Volatility Index (FXVIX) as a measure of FX volatility. In particular, the three major FXVIX indices (EUVIX, BPVIX, and JYVIX) from 2010 to 2019 are considered, and we predict future prices using the proposed hybrid model. Our hybrid model utilizes an LSTM model as an encoder and decoder inside an autoencoder network. Additionally, we investigate FXVIX indices through subperiod analysis to examine how the proposed model’s forecasting performance is influenced by data distributions and outliers. Based on the empirical results, we can conclude that the proposed hybrid method, which we call the autoencoder-LSTM model, outperforms the traditional LSTM method. Additionally, the ability to learn the magnitude of data spread and singularities determines the accuracy of predictions made using deep learning models. In summary, this study established that FX volatility can be accurately predicted using a combination of deep learning models. Our findings have important implications for practitioners. Because forecasting volatility is an essential task for financial decision-making, this study will enable traders and policymakers to hedge or invest efficiently and make policy decisions based on volatility forecasting.
DA  - 2021/03/31/
PY  - 2021
DO  - 10.1155/2021/6647534
DP  - DOI.org (Crossref)
VL  - 2021
SP  - 1
EP  - 16
J2  - Complexity
LA  - en
SN  - 1099-0526, 1076-2787
UR  - https://www.hindawi.com/journals/complexity/2021/6647534/
Y2  - 2021/12/07/03:25:03
L4  - https://downloads.hindawi.com/journals/complexity/2021/6647534.pdf
ER  - 

TY  - JOUR
TI  - Deep learning models for forecasting and analyzing the implications of COVID-19 spread on some commodities markets volatilities
AU  - Sadefo Kamdem, Jules
AU  - Bandolo Essomba, Rose
AU  - Njong Berinyuy, James
T2  - Chaos, Solitons & Fractals
DA  - 2020/11//
PY  - 2020
DO  - 10.1016/j.chaos.2020.110215
DP  - DOI.org (Crossref)
VL  - 140
SP  - 110215
J2  - Chaos, Solitons & Fractals
LA  - en
SN  - 09600779
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0960077920306111
Y2  - 2021/12/07/03:26:03
L4  - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7437517/pdf/main.pdf
ER  - 

TY  - JOUR
TI  - Neural network heterogeneous autoregressive models for realized volatility
AU  - Kim, Jaiyool
AU  - Baek, Changryong
T2  - Communications for Statistical Applications and Methods
DA  - 2018/11/30/
PY  - 2018
DO  - 10.29220/CSAM.2018.25.6.659
DP  - DOI.org (Crossref)
VL  - 25
IS  - 6
SP  - 659
EP  - 671
J2  - CSAM
LA  - en
SN  - 2383-4757
UR  - http://www.csam.or.kr/journal/view.html?doi=10.29220/CSAM.2018.25.6.659
Y2  - 2021/12/07/03:29:08
L4  - http://www.csam.or.kr/journal/download_pdf.php?doi=10.29220/CSAM.2018.25.6.659
ER  - 

TY  - JOUR
TI  - A dynamic target volatility strategy for asset allocation using artificial neural networks
AU  - Kim, Youngmin
AU  - Enke, David
T2  - The Engineering Economist
DA  - 2018/10/02/
PY  - 2018
DO  - 10.1080/0013791X.2018.1461287
DP  - DOI.org (Crossref)
VL  - 63
IS  - 4
SP  - 273
EP  - 290
J2  - The Engineering Economist
LA  - en
SN  - 0013-791X, 1547-2701
UR  - https://www.tandfonline.com/doi/full/10.1080/0013791X.2018.1461287
Y2  - 2021/12/07/03:29:47
ER  - 

TY  - JOUR
TI  - Gold price volatility: A forecasting approach using the Artificial Neural Network–GARCH model
AU  - Kristjanpoller, Werner
AU  - Minutolo, Marcel C.
T2  - Expert Systems with Applications
DA  - 2015/11//
PY  - 2015
DO  - 10.1016/j.eswa.2015.04.058
DP  - DOI.org (Crossref)
VL  - 42
IS  - 20
SP  - 7245
EP  - 7251
J2  - Expert Systems with Applications
LA  - en
SN  - 09574174
ST  - Gold price volatility
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0957417415003000
Y2  - 2021/12/07/03:30:52
ER  - 

TY  - JOUR
TI  - Forecasting volatility of oil price using an artificial neural network-GARCH model
AU  - Kristjanpoller, Werner
AU  - Minutolo, Marcel C.
T2  - Expert Systems with Applications
DA  - 2016/12//
PY  - 2016
DO  - 10.1016/j.eswa.2016.08.045
DP  - DOI.org (Crossref)
VL  - 65
SP  - 233
EP  - 241
J2  - Expert Systems with Applications
LA  - en
SN  - 09574174
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0957417416304420
Y2  - 2021/12/07/03:31:40
ER  - 

TY  - JOUR
TI  - A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis and principal components analysis
AU  - Kristjanpoller, Werner
AU  - Minutolo, Marcel C.
T2  - Expert Systems with Applications
DA  - 2018/11//
PY  - 2018
DO  - 10.1016/j.eswa.2018.05.011
DP  - DOI.org (Crossref)
VL  - 109
SP  - 1
EP  - 11
J2  - Expert Systems with Applications
LA  - en
SN  - 09574174
UR  - https://linkinghub.elsevier.com/retrieve/pii/S095741741830294X
Y2  - 2021/12/07/03:33:43
ER  - 

TY  - JOUR
TI  - Volatility forecast using hybrid Neural Network models
AU  - Kristjanpoller, Werner
AU  - Fadic, Anton
AU  - Minutolo, Marcel C.
T2  - Expert Systems with Applications
DA  - 2014/04//
PY  - 2014
DO  - 10.1016/j.eswa.2013.09.043
DP  - DOI.org (Crossref)
VL  - 41
IS  - 5
SP  - 2437
EP  - 2442
J2  - Expert Systems with Applications
LA  - en
SN  - 09574174
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0957417413007975
Y2  - 2021/12/07/03:34:24
ER  - 

TY  - JOUR
TI  - Performance of Deep Learning in Prediction of Stock Market Volatility
AU  - Moon, Kyoung-Sook
AU  - Kim, Hongjoong
T2  - ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH
DA  - 2019/06/16/
PY  - 2019
DO  - 10.24818/18423264/53.2.19.05
DP  - DOI.org (Crossref)
VL  - 53
IS  - 2/2019
SP  - 77
EP  - 92
J2  - ECECSR
SN  - 0424-267X, 1842-3264
UR  - http://ecocyb.ase.ro/nr2019_2/5.%20Kyoung-Sook%20MOON,Hongjoong%20KIM%20(T).pdf
Y2  - 2021/12/07/03:35:43
L4  - http://ecocyb.ase.ro/nr2019_2/5.%20Kyoung-Sook%20MOON,Hongjoong%20KIM%20(T).pdf
ER  - 

TY  - JOUR
TI  - On stock volatility forecasting based on text mining and deep learning under high‐frequency data
AU  - Lei, Bolin
AU  - Liu, Zhengdi
AU  - Song, Yuping
T2  - Journal of Forecasting
DA  - 2021/12//
PY  - 2021
DO  - 10.1002/for.2794
DP  - DOI.org (Crossref)
VL  - 40
IS  - 8
SP  - 1596
EP  - 1610
J2  - Journal of Forecasting
LA  - en
SN  - 0277-6693, 1099-131X
UR  - https://onlinelibrary.wiley.com/doi/10.1002/for.2794
Y2  - 2021/12/07/13:01:16
ER  - 

TY  - JOUR
TI  - Volatility Forecasting for High-Frequency Financial Data Based on Web Search Index and Deep Learning Model
AU  - Lei, Bolin
AU  - Zhang, Boyu
AU  - Song, Yuping
T2  - Mathematics
AB  - The existing index system for volatility forecasting only focuses on asset return series or historical volatility, and the prediction model cannot effectively describe the highly complex and nonlinear characteristics of the stock market. In this study, we construct an investor attention factor through a Baidu search index of antecedent keywords, and then combine other trading information such as the trading volume, trend indicator, quote change rate, etc., as input indicators, and finally employ the deep learning model via temporal convolutional networks (TCN) to forecast the volatility under high-frequency financial data. We found that the prediction accuracy of the TCN model with investor attention is better than those of the TCN model without investor attention, the traditional econometric model as the generalized autoregressive conditional heteroscedasticity (GARCH), the heterogeneous autoregressive model of realized volatility (HAR-RV), autoregressive fractionally integrated moving average (ARFIMA) models, and the long short-term memory (LSTM) model with investor attention. Compared with the traditional econometric models, the multi-step prediction results for the TCN model remain robust. Our findings provide a more accurate and robust method for volatility forecasting for big data and enrich the index system of volatility forecasting.
DA  - 2021/02/05/
PY  - 2021
DO  - 10.3390/math9040320
DP  - DOI.org (Crossref)
VL  - 9
IS  - 4
SP  - 320
J2  - Mathematics
LA  - en
SN  - 2227-7390
UR  - https://www.mdpi.com/2227-7390/9/4/320
Y2  - 2021/12/07/13:02:37
L4  - https://www.mdpi.com/2227-7390/9/4/320/pdf
ER  - 

TY  - JOUR
TI  - The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach
AU  - Li, Yuze
AU  - Jiang, Shangrong
AU  - Li, Xuerong
AU  - Wang, Shouyang
T2  - Energy Economics
DA  - 2021/03//
PY  - 2021
DO  - 10.1016/j.eneco.2021.105140
DP  - DOI.org (Crossref)
VL  - 95
SP  - 105140
J2  - Energy Economics
LA  - en
SN  - 01409883
ST  - The role of news sentiment in oil futures returns and volatility forecasting
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0140988321000451
Y2  - 2021/12/07/13:05:15
ER  - 

TY  - JOUR
TI  - Exchange Rate Volatility Forecasting by Hybrid Neural Network Markov Switching Beta-t-EGARCH
AU  - Liao, Ruofan
AU  - Yamaka, Woraphon
AU  - Sriboonchitta, Songsak
T2  - IEEE Access
DA  - 2020///
PY  - 2020
DO  - 10.1109/ACCESS.2020.3038564
DP  - DOI.org (Crossref)
VL  - 8
SP  - 207563
EP  - 207574
J2  - IEEE Access
SN  - 2169-3536
UR  - https://ieeexplore.ieee.org/document/9261362/
Y2  - 2021/12/07/13:06:07
L4  - https://ieeexplore.ieee.org/ielx7/6287639/8948470/09261362.pdf
ER  - 

TY  - JOUR
TI  - Pricing Options and Computing Implied Volatilities using Neural Networks
AU  - Liu, Shuaiqiang
AU  - Oosterlee, Cornelis
AU  - Bohte, Sander
T2  - Risks
AB  - This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function approximators, this method trains an optimized ANN on a data set generated by a sophisticated financial model, and runs the trained ANN as an agent of the original solver in a fast and efficient way. We test this approach on three different types of solvers, including the analytic solution for the Black-Scholes equation, the COS method for the Heston stochastic volatility model and Brent’s iterative root-finding method for the calculation of implied volatilities. The numerical results show that the ANN solver can reduce the computing time significantly.
DA  - 2019/02/09/
PY  - 2019
DO  - 10.3390/risks7010016
DP  - DOI.org (Crossref)
VL  - 7
IS  - 1
SP  - 16
J2  - Risks
LA  - en
SN  - 2227-9091
UR  - http://www.mdpi.com/2227-9091/7/1/16
Y2  - 2021/12/07/13:10:28
L4  - https://www.mdpi.com/2227-9091/7/1/16/pdf
ER  - 

TY  - JOUR
TI  - Novel volatility forecasting using deep learning–Long Short Term Memory Recurrent Neural Networks
AU  - Liu, Yang
T2  - Expert Systems with Applications
DA  - 2019/10//
PY  - 2019
DO  - 10.1016/j.eswa.2019.04.038
DP  - DOI.org (Crossref)
VL  - 132
SP  - 99
EP  - 109
J2  - Expert Systems with Applications
LA  - en
SN  - 09574174
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0957417419302635
Y2  - 2021/12/07/13:13:11
ER  - 

TY  - JOUR
TI  - Volatility Degree Forecasting of Stock Market by Stochastic Time Strength Neural Network
AU  - Mo, Haiyan
AU  - Wang, Jun
T2  - Mathematical Problems in Engineering
AB  - In view of the applications of artificial neural networks in economic and financial forecasting, a stochastic time strength function is introduced in the backpropagation neural network model to predict the fluctuations of stock price changes. In this model, stochastic time strength function gives a weight for each historical datum and makes the model have the effect of random movement, and then we investigate and forecast the behavior of volatility degrees of returns for the Chinese stock market indexes and some global market indexes. The empirical research is performed in testing the prediction effect of SSE, SZSE, HSI, DJIA, IXIC, and S&P 500 with different selected volatility degrees in the established model.
DA  - 2013///
PY  - 2013
DO  - 10.1155/2013/436795
DP  - DOI.org (Crossref)
VL  - 2013
SP  - 1
EP  - 11
J2  - Mathematical Problems in Engineering
LA  - en
SN  - 1024-123X, 1563-5147
UR  - http://www.hindawi.com/journals/mpe/2013/436795/
Y2  - 2021/12/07/13:16:08
L4  - https://downloads.hindawi.com/journals/mpe/2013/436795.pdf
ER  - 

TY  - JOUR
TI  - Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach
AU  - Othman, Anwar Hasan Abdullah
AU  - Kassim, Salina
AU  - Rosman, Romzie Bin
AU  - Redzuan, Nur Harena Binti
T2  - Journal of Revenue and Pricing Management
DA  - 2020/10//
PY  - 2020
DO  - 10.1057/s41272-020-00229-3
DP  - DOI.org (Crossref)
VL  - 19
IS  - 5
SP  - 314
EP  - 330
J2  - J Revenue Pricing Manag
LA  - en
SN  - 1476-6930, 1477-657X
UR  - http://link.springer.com/10.1057/s41272-020-00229-3
Y2  - 2021/12/07/13:17:15
ER  - 

TY  - JOUR
TI  - Comments and further improvements on “pth moment stability of stochastic neural networks with Markov volatilities”
AU  - Zhu, Enwen
AU  - Yang, Gang
AU  - Liu, Jun
T2  - Neural Computing and Applications
DA  - 2013/09//
PY  - 2013
DO  - 10.1007/s00521-013-1396-9
DP  - DOI.org (Crossref)
VL  - 23
IS  - 3-4
SP  - 1179
EP  - 1183
J2  - Neural Comput & Applic
LA  - en
SN  - 0941-0643, 1433-3058
UR  - http://link.springer.com/10.1007/s00521-013-1396-9
Y2  - 2021/12/06/19:59:08
ER  - 

TY  - JOUR
TI  - A neural network enhanced volatility component model
AU  - Zhai, Jia
AU  - Cao, Yi
AU  - Liu, Xiaoquan
T2  - Quantitative Finance
DA  - 2020/05/03/
PY  - 2020
DO  - 10.1080/14697688.2019.1711148
DP  - DOI.org (Crossref)
VL  - 20
IS  - 5
SP  - 783
EP  - 797
J2  - Quantitative Finance
LA  - en
SN  - 1469-7688, 1469-7696
UR  - https://www.tandfonline.com/doi/full/10.1080/14697688.2019.1711148
Y2  - 2021/12/06/
L4  - https://eprints.nottingham.ac.uk/60139/1/Xiaoquan-merged.pdf
ER  - 

TY  - JOUR
TI  - Online adaptive machine learning based algorithm for implied volatility surface modeling
AU  - Zeng, Yaxiong
AU  - Klabjan, Diego
T2  - Knowledge-Based Systems
DA  - 2019/01//
PY  - 2019
DO  - 10.1016/j.knosys.2018.08.039
DP  - DOI.org (Crossref)
VL  - 163
SP  - 376
EP  - 391
J2  - Knowledge-Based Systems
LA  - en
SN  - 09507051
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0950705118304350
Y2  - 2021/12/06/
L4  - https://arxiv.org/pdf/1706.01833
ER  - 

TY  - JOUR
TI  - Big data analytics for financial Market volatility forecast based on support vector machine
AU  - Yang, Rongjun
AU  - Yu, Lin
AU  - Zhao, Yuanjun
AU  - Yu, Hongxin
AU  - Xu, Guiping
AU  - Wu, Yiting
AU  - Liu, Zhengkai
T2  - International Journal of Information Management
DA  - 2020/02//
PY  - 2020
DO  - 10.1016/j.ijinfomgt.2019.05.027
DP  - DOI.org (Crossref)
VL  - 50
SP  - 452
EP  - 462
J2  - International Journal of Information Management
LA  - en
SN  - 02684012
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0268401218313604
Y2  - 2021/12/06/
ER  - 

TY  - CONF
TI  - Stock volatility prediction based on convolutional neural network
AU  - Xu, Lili
T2  - BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY
DA  - 2021///
PY  - 2021
VL  - 128
SP  - 178
EP  - 178
PB  - WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
SN  - 1742-7835
ER  - 

TY  - JOUR
TI  - Development of a simple unified volatility-based scheme (SUVS) for secondary organic aerosol formation using genetic algorithms
AU  - Xia, A. G.
AU  - Stroud, C. A.
AU  - Makar, P. A.
T2  - Atmospheric Chemistry and Physics
AB  - Abstract. A new method is proposed to simplify complex atmospheric chemistry reaction schemes, while preserving SOA formation properties, using genetic algorithms. The method is first applied in this study to the gas-phase α-pinene oxidation scheme. The simple unified volatility-based scheme (SUVS) reflects the multi-generation evolution of chemical species from a near-explicit master chemical mechanism (MCM) and, at the same time, uses the volatility-basis set speciation for condensable products. The SUVS also unifies reactions between SOA precursors with different oxidants under different atmospheric conditions. A total of 412 unknown parameters (product yields of parameterized products, reaction rates, etc.) from the SUVS are estimated by using genetic algorithms operating on the detailed mechanism. The number of organic species was reduced from 310 in the detailed mechanism to 31 in the SUVS. Output species profiles, obtained from the original subset of the MCM reaction scheme for α-pinene oxidation, are reproduced with maximum fractional error at 0.10 for scenarios under a wide range of ambient HC/NOx conditions. Ultimately, the same SUVS with updated parameters could be used to describe the SOA formation from different precursors.
DA  - 2011/07/01/
PY  - 2011
DO  - 10.5194/acp-11-6185-2011
DP  - DOI.org (Crossref)
VL  - 11
IS  - 13
SP  - 6185
EP  - 6205
J2  - Atmos. Chem. Phys.
LA  - en
SN  - 1680-7324
UR  - https://acp.copernicus.org/articles/11/6185/2011/
Y2  - 2021/12/06/
L4  - https://www.atmos-chem-phys.net/11/6185/2011/acp-11-6185-2011.pdf
ER  - 

TY  - JOUR
TI  - AN ADAPTIVE EXPECTATION GENETIC ALGORITHM BASED ON ANFIS AND MULTINATIONAL STOCK MARKET VOLATILITY CAUSALITY FOR TAIEX FORECASTING
AU  - Wei, Liang-Ying
T2  - Cybernetics and Systems
DA  - 2012/06//
PY  - 2012
DO  - 10.1080/01969722.2012.688687
DP  - DOI.org (Crossref)
VL  - 43
IS  - 5
SP  - 410
EP  - 425
J2  - Cybernetics and Systems
LA  - en
SN  - 0196-9722, 1087-6553
UR  - http://www.tandfonline.com/doi/abs/10.1080/01969722.2012.688687
Y2  - 2021/12/06/
ER  - 

TY  - JOUR
TI  - Determining the invoicing dates for raw material order and finish product dispatch using neural networks under exchange rate volatility
AU  - Weerasingha, Janith Piyumal
AU  - Bandara, Yapa Mahinda
AU  - Edirisinghe, Pasan Manuranga
T2  - International Journal of Logistics Research and Applications
DA  - 2021/07/03/
PY  - 2021
DO  - 10.1080/13675567.2021.1945018
DP  - DOI.org (Crossref)
SP  - 1
EP  - 21
J2  - International Journal of Logistics Research and Applications
LA  - en
SN  - 1367-5567, 1469-848X
UR  - https://www.tandfonline.com/doi/full/10.1080/13675567.2021.1945018
Y2  - 2021/12/06/
ER  - 

TY  - JOUR
TI  - Stock Volatility Prediction by Hybrid Neural Network
AU  - Wang, Yujie
AU  - Liu, Hui
AU  - Guo, Qiang
AU  - Xie, Shenxiang
AU  - Zhang, Xiaofeng
T2  - IEEE Access
DA  - 2019///
PY  - 2019
DO  - 10.1109/ACCESS.2019.2949074
DP  - DOI.org (Crossref)
VL  - 7
SP  - 154524
EP  - 154534
J2  - IEEE Access
SN  - 2169-3536
UR  - https://ieeexplore.ieee.org/document/8880597/
Y2  - 2021/12/06/
L4  - https://ieeexplore.ieee.org/ielx7/6287639/8600701/08880597.pdf
ER  - 

TY  - JOUR
TI  - Using neural network for forecasting TXO price under different volatility models
AU  - Wang, Ching-Ping
AU  - Lin, Shin-Hung
AU  - Huang, Hung-Hsi
AU  - Wu, Pei-Chen
T2  - Expert Systems with Applications
DA  - 2012/04//
PY  - 2012
DO  - 10.1016/j.eswa.2011.11.038
DP  - DOI.org (Crossref)
VL  - 39
IS  - 5
SP  - 5025
EP  - 5032
J2  - Expert Systems with Applications
LA  - en
SN  - 09574174
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0957417411015818
Y2  - 2021/12/06/
ER  - 

TY  - JOUR
TI  - A support vector machine based MSM model for financial short-term volatility forecasting
AU  - Wang, Baohua
AU  - Huang, Hejiao
AU  - Wang, Xiaolong
T2  - Neural Computing and Applications
AB  - Financial time series forecasting has become a challenge because of its long-memory, thick tails and volatility persistence. Multifractal process has recently been proposed as a new formalism for this problem. An iterative Markov-Switching Multifractal (MSM) model was introduced to the literature. It is able to capture many of the important stylized features of the financial time series, including long-memory in volatility, volatility clustering, and return outliers. The model delivers stronger performance both in- and out-of-sample than GARCH-type models in long-term forecasts. To enhance MSM’s short-term prediction accuracy, this paper proposes a support vector machine (SVM) based MSM approach which exploits MSM model to forecast volatility and SVM to model the innovations. To verify the effectiveness of the proposed approach, two stock indexes in the Chinese A-share market are chosen as the forecasting targets. Comparing with some existing state-of-the-art models, the proposed approach gives superior results. It indicates that the proposed model provides a promising alternative to financial short-term volatility prediction.
DA  - 2013/01/01/
PY  - 2013
DO  - 10.1007/s00521-011-0742-z
DP  - Springer Link
VL  - 22
IS  - 1
SP  - 21
EP  - 28
J2  - Neural Comput & Applic
LA  - en
SN  - 1433-3058
UR  - https://doi.org/10.1007/s00521-011-0742-z
Y2  - 2021/12/06/
ER  - 

TY  - JOUR
TI  - Intelligent financial management of company based on neural network and fuzzy volatility evaluation
AU  - Wang, Aiqun
AU  - Liu, Yaona
T2  - Journal of Intelligent & Fuzzy Systems
AB  - By integrating business processes, financial intelligent management system can provide effective data information with high quality and low cost for strategic decision-making. In this paper, the authors analyze the intelligent financial management of
DA  - 2020/01/01/
PY  - 2020
DO  - 10.3233/JIFS-179798
DP  - content.iospress.com
VL  - 38
IS  - 6
SP  - 7215
EP  - 7228
LA  - en
SN  - 1064-1246
UR  - https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs179798
Y2  - 2021/12/06/
L2  - https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs179798
ER  - 

TY  - JOUR
TI  - Implied volatility directional forecasting: a machine learning approach
AU  - Vrontos, Spyridon D.
AU  - Galakis, John
AU  - Vrontos, Ioannis D.
T2  - Quantitative Finance
DA  - 2021/10/03/
PY  - 2021
DO  - 10.1080/14697688.2021.1905869
DP  - DOI.org (Crossref)
VL  - 21
IS  - 10
SP  - 1687
EP  - 1706
J2  - Quantitative Finance
LA  - en
SN  - 1469-7688, 1469-7696
ST  - Implied volatility directional forecasting
UR  - https://www.tandfonline.com/doi/full/10.1080/14697688.2021.1905869
Y2  - 2021/12/06/
L4  - https://www.tandfonline.com/doi/pdf/10.1080/14697688.2021.1905869?needAccess=true
ER  - 

TY  - JOUR
TI  - Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH
AU  - Vortelinos, Dimitrios I.
T2  - Research in International Business and Finance
T3  - Special Issue articles on Finance Reconsidered edited by Dr. Thomas Lagoarde-Segot and Dr. Bernard Paranque, Special issue articles on Recent trends and challenges in financial and commodity markets Edited by Prof. Fredj Jawadi and Prof. Benoît Sevi & Special Issue articles on Recent Topics in Banking and Finance: New Findings and Implications Edited by Prof. Fredj Jawadi and Prof. Wael Louhichi
AB  - This paper examines whether nonlinear models, like Principal Components Combining, neural networks and GARCH are more accurate on realized volatility forecasting than the Heterogeneous Autoregressive (HAR) model. The answer is no. The realized volatility property of persistence is too important to leave out of a realized volatility forecasting model. However, the Principal Components Combining model is ranked very close to HAR. Analysis is implemented in seven US financial markets: spot equity, spot foreign exchange rates, exchange traded funds, equity index futures, US Treasury bonds futures, energy futures, and commodities options.
DA  - 2017/01/01/
PY  - 2017
DO  - 10.1016/j.ribaf.2015.01.004
DP  - ScienceDirect
VL  - 39
SP  - 824
EP  - 839
J2  - Research in International Business and Finance
LA  - en
SN  - 0275-5319
ST  - Forecasting realized volatility
UR  - https://www.sciencedirect.com/science/article/pii/S0275531915000057
Y2  - 2021/12/06/
L2  - https://www.sciencedirect.com/science/article/abs/pii/S0275531915000057
KW  - Forecasting
KW  - Neural networks
KW  - Principal Components Combining
ER  - 

TY  - JOUR
TI  - Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach
AU  - Tung, W.L.
AU  - Quek, C.
T2  - Expert Systems with Applications
DA  - 2011/05//
PY  - 2011
DO  - 10.1016/j.eswa.2010.07.116
DP  - DOI.org (Crossref)
VL  - 38
IS  - 5
SP  - 4668
EP  - 4688
J2  - Expert Systems with Applications
LA  - en
SN  - 09574174
UR  - https://linkinghub.elsevier.com/retrieve/pii/S095741741000744X
Y2  - 2021/12/06/
L4  - https://europepmc.org/articles/pmc7126939?pdf=render
ER  - 

TY  - JOUR
TI  - Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices
AU  - Tseng, Chih-Hsiung
AU  - Cheng, Sheng-Tzong
AU  - Wang, Yi-Hsien
AU  - Peng, Jin-Tang
T2  - Physica A: Statistical Mechanics and its Applications
DA  - 2008/05//
PY  - 2008
DO  - 10.1016/j.physa.2008.01.074
DP  - DOI.org (Crossref)
VL  - 387
IS  - 13
SP  - 3192
EP  - 3200
J2  - Physica A: Statistical Mechanics and its Applications
LA  - en
SN  - 03784371
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0378437108000320
Y2  - 2021/12/06/
ER  - 

TY  - JOUR
TI  - Financial volatility trading using recurrent neural networks
AU  - Tino, P.
AU  - Schittenkopf, C.
AU  - Dorffner, G.
T2  - IEEE Transactions on Neural Networks
DA  - 2001/07//
PY  - 2001
DO  - 10.1109/72.935096
DP  - DOI.org (Crossref)
VL  - 12
IS  - 4
SP  - 865
EP  - 874
J2  - IEEE Trans. Neural Netw.
SN  - 10459227
UR  - http://ieeexplore.ieee.org/document/935096/
Y2  - 2021/12/06/
ER  - 

TY  - JOUR
TI  - Forecasting volatility based on wavelet support vector machine
AU  - Tang, Ling-Bing
AU  - Tang, Ling-Xiao
AU  - Sheng, Huan-Ye
T2  - Expert Systems with Applications
DA  - 2009/03//
PY  - 2009
DO  - 10.1016/j.eswa.2008.01.047
DP  - DOI.org (Crossref)
VL  - 36
IS  - 2
SP  - 2901
EP  - 2909
J2  - Expert Systems with Applications
LA  - en
SN  - 09574174
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0957417408000511
Y2  - 2021/12/06/
ER  - 

TY  - JOUR
TI  - Calibrating rough volatility models: a convolutional neural network approach
AU  - Stone, Henry
T2  - Quantitative Finance
DA  - 2020/03/03/
PY  - 2020
DO  - 10.1080/14697688.2019.1654126
DP  - DOI.org (Crossref)
VL  - 20
IS  - 3
SP  - 379
EP  - 392
J2  - Quantitative Finance
LA  - en
SN  - 1469-7688, 1469-7696
ST  - Calibrating rough volatility models
UR  - https://www.tandfonline.com/doi/full/10.1080/14697688.2019.1654126
Y2  - 2021/12/06/
L4  - https://arxiv.org/pdf/1812.05315
ER  - 

TY  - CHAP
TI  - Forecasting the Volatility of Stock Index Returns: A Stochastic Neural Network Approach
AU  - Slim, Chokri
T2  - Computational Science and Its Applications – ICCSA 2004
A2  - Laganá, Antonio
A2  - Gavrilova, Marina L.
A2  - Kumar, Vipin
A2  - Mun, Youngsong
A2  - Tan, C. J. Kenneth
A2  - Gervasi, Osvaldo
A3  - Kanade, Takeo
A3  - Kittler, Josef
A3  - Kleinberg, Jon M.
A3  - Mattern, Friedemann
A3  - Mitchell, John C.
A3  - Nierstrasz, Oscar
A3  - Pandu Rangan, C.
A3  - Steffen, Bernhard
A3  - Sudan, Madhu
A3  - Terzopoulos, Demetri
A3  - Tygar, Dough
A3  - Vardi, Moshe Y.
A3  - Weikum, Gerhard
CY  - Berlin, Heidelberg
DA  - 2004///
PY  - 2004
DP  - DOI.org (Crossref)
VL  - 3045
SP  - 935
EP  - 944
PB  - Springer Berlin Heidelberg
SN  - 978-3-540-22057-2 978-3-540-24767-8
ST  - Forecasting the Volatility of Stock Index Returns
UR  - http://link.springer.com/10.1007/978-3-540-24767-8_98
Y2  - 2021/12/06/
ER  - 

TY  - JOUR
TI  - Hybrid Forecasting Models Based on the Neural Networks for the Volatility of Bitcoin
AU  - Seo, Monghwan
AU  - Kim, Geonwoo
T2  - Applied Sciences
AB  - In this paper, we study the volatility forecasts in the Bitcoin market, which has become popular in the global market in recent years. Since the volatility forecasts help trading decisions of traders who want a profit, the volatility forecasting is an important task in the market. For the improvement of the forecasting accuracy of Bitcoin’s volatility, we develop the hybrid forecasting models combining the GARCH family models with the machine learning (ML) approach. Specifically, we adopt Artificial Neural Network (ANN) and Higher Order Neural Network (HONN) for the ML approach and construct the hybrid models using the outputs of the GARCH models and several relevant variables as input variables. We carry out many experiments based on the proposed models and compare the forecasting accuracy of the models. In addition, we provide the Model Confidence Set (MCS) test to find statistically the best model. The results show that the hybrid models based on HONN provide more accurate forecasts than the other models.
DA  - 2020/07/10/
PY  - 2020
DO  - 10.3390/app10144768
DP  - DOI.org (Crossref)
VL  - 10
IS  - 14
SP  - 4768
J2  - Applied Sciences
LA  - en
SN  - 2076-3417
UR  - https://www.mdpi.com/2076-3417/10/14/4768
Y2  - 2021/12/06/
L4  - https://res.mdpi.com/d_attachment/applsci/applsci-10-04768/article_deploy/applsci-10-04768-v2.pdf
ER  - 

TY  - JOUR
TI  - Volatility Forecasting Using Support Vector Regression and a Hybrid Genetic Algorithm
AU  - Santamaría-Bonfil, Guillermo
AU  - Frausto-Solís, Juan
AU  - Vázquez-Rodarte, Ignacio
T2  - Computational Economics
AB  - Volatility forecasting is an important process required to measure variability in equity prices, risk management, and several other financial activities. Generalized autoregressive conditional heteroscedastic methods $$(\textit{GARCH})$$have been used to forecast volatility with reasonable success due unreal assumptions about volatility underlying process. Recently, a supervised learning machine called support vector regression $$(SVR)$$has been employed to forecast financial volatility. Nevertheless, the quality and stability of the model obtained through $$SVR$$training process depend strongly on the selection of $$SVR$$parameters. Typically, these are tuned by a grid search method $$(SVR_{GS})$$; however, this tuning procedure is prone to get trapped on local optima, requires a priori information, and it does not concurrently tune the kernels and its parameters. This paper presents a new method called $$SVR_{GBC}$$for the financial volatility forecasting problem which selects simultaneously the proper kernel and its parameter values. $$SVR_{GBC}$$is a hybrid genetic algorithm which uses several genetic operators to enhance the exploration of solutions space: it introduces a new genetic operator called Boltzmann selection, and the use of several random number generators. Experimental data correspond to two ASEAN and two latinoamerican market indexes. $$SVR_{GBC}$$results are compared against $$\textit{GARCH}\left( 1,1\right) \hbox { and }SVR_{GS}$$method. It uses the mean absolute percentage error and directional accuracy functions for measuring quality results. Experimentation shows that, in general, $$SVR_{GBC}$$overcomes quality of $$\textit{GARCH}\left( 1,1\right) \hbox { and }SVR_{GS}$$.
DA  - 2015/01/01/
PY  - 2015
DO  - 10.1007/s10614-013-9411-x
DP  - Springer Link
VL  - 45
IS  - 1
SP  - 111
EP  - 133
J2  - Comput Econ
LA  - en
SN  - 1572-9974
UR  - https://doi.org/10.1007/s10614-013-9411-x
Y2  - 2021/12/06/
ER  - 

TY  - JOUR
TI  - Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network
AU  - Ramos-Pérez, Eduardo
AU  - Alonso-González, Pablo J.
AU  - Núñez-Velázquez, José Javier
T2  - Expert Systems with Applications
DA  - 2019/09//
PY  - 2019
DO  - 10.1016/j.eswa.2019.03.046
DP  - DOI.org (Crossref)
VL  - 129
SP  - 1
EP  - 9
J2  - Expert Systems with Applications
LA  - en
SN  - 09574174
UR  - https://linkinghub.elsevier.com/retrieve/pii/S0957417419302209
Y2  - 2021/12/06/
L4  - https://arxiv.org/pdf/2006.16383
ER  - 

TY  - JOUR
TI  - Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods
AU  - Rahman, Quazi Abidur
AU  - Janmohamed, Tahir
AU  - Pirbaglou, Meysam
AU  - Clarke, Hance
AU  - Ritvo, Paul
AU  - Heffernan, Jane M.
AU  - Katz, Joel
T2  - Journal of Medical Internet Research
AB  - Background: Measuring and predicting pain volatility (fluctuation or variability in pain scores over time) can help improve pain management. Perceptions of pain and its consequent disabling effects are often heightened under the conditions of greater uncertainty and unpredictability associated with pain volatility. Objective: This study aimed to use data mining and machine learning methods to (1) define a new measure of pain volatility and (2) predict future pain volatility levels from users of the pain management app, Manage My Pain, based on demographic, clinical, and app use features. Methods: Pain volatility was defined as the mean of absolute changes between 2 consecutive self-reported pain severity scores within the observation periods. The k-means clustering algorithm was applied to users’ pain volatility scores at the first and sixth month of app use to establish a threshold discriminating low from high volatility classes. Subsequently, we extracted 130 demographic, clinical, and app usage features from the first month of app use to predict these 2 volatility classes at the sixth month of app use. Prediction models were developed using 4 methods: (1) logistic regression with ridge estimators; (2) logistic regression with Least Absolute Shrinkage and Selection Operator; (3) Random Forests; and (4) Support Vector Machines. Overall prediction accuracy and accuracy for both classes were calculated to compare the performance of the prediction models. Training and testing were conducted using 5-fold cross validation. A class imbalance issue was addressed using a random subsampling of the training dataset. Users with at least five pain records in both the predictor and outcome periods (N=782 users) are included in the analysis. Results: k-means clustering algorithm was applied to pain volatility scores to establish a threshold of 1.6 to differentiate between low and high volatility classes. After validating the threshold using random subsamples, 2 classes were created: low volatility (n=611) and high volatility (n=171). In this class-imbalanced dataset, all 4 prediction models achieved 78.1% (611/782) to 79.0% (618/782) in overall accuracy. However, all models have a prediction accuracy of less than 18.7% (32/171) for the high volatility class. After addressing the class imbalance issue using random subsampling, results improved across all models for the high volatility class to greater than 59.6% (102/171). The prediction model based on Random Forests performs the best as it consistently achieves approximately 70% accuracy for both classes across 3 random subsamples. Conclusions: We propose a novel method for measuring pain volatility. Cluster analysis was applied to divide users into subsets of low and high volatility classes. These classes were then predicted at the sixth month of app use with an acceptable degree of accuracy using machine learning methods based on the features extracted from demographic, clinical, and app use information from the first month.
DA  - 2018/11/15/
PY  - 2018
DO  - 10.2196/12001
DP  - www.jmir.org
VL  - 20
IS  - 11
SP  - e12001
LA  - EN
ST  - Defining and Predicting Pain Volatility in Users of the Manage My Pain App
UR  - https://www.jmir.org/2018/11/e12001
Y2  - 2021/12/06/
L2  - https://www.jmir.org/2018/11/e12001/
ER  - 

TY  - JOUR
TI  - Modeling Realized Volatility Dynamics with a Genetic Algorithm
AU  - Qu, Hui
AU  - Ji, Ping
T2  - Journal of Forecasting
AB  - The heterogeneous autoregressive model of realized volatility (HAR-RV) is inspired by the heterogeneous market hypothesis and characterizes realized volatility dynamics through a linear function of lagged daily, weekly and monthly realized volatilities with a (1, 5, 22) lag structure. Considering that different markets can have different heterogeneous structures and a market's heterogeneous structure can vary over time, we build an adaptive heterogeneous autoregressive model of realized volatility (AHAR-RV), whose lag structure is optimized with a genetic algorithm. Using nine common loss functions and the superior predictive ability test, we find that our AHAR-RV model and its extensions provide significantly better out-of-sample volatility forecasts for the CSI 300 index than the corresponding HAR models. Furthermore, the AHAR-RV model significantly outperforms all the other models under most loss functions. Besides, we confirm that Chinese stock markets' heterogeneous structure varies over time and the (1, 5, 22) lag structure is not the optimal choice. Copyright © 2016 John Wiley & Sons, Ltd.
DA  - 2016///
PY  - 2016
DO  - 10.1002/for.2386
DP  - Wiley Online Library
VL  - 35
IS  - 5
SP  - 434
EP  - 444
LA  - en
SN  - 1099-131X
UR  - https://onlinelibrary.wiley.com/doi/abs/10.1002/for.2386
Y2  - 2021/12/06/
L2  - https://onlinelibrary.wiley.com/doi/abs/10.1002/for.2386
KW  - genetic algorithm
KW  - heterogeneous autoregressive model
KW  - realized volatility
KW  - superior predictive ability test
ER  - 

TY  - JOUR
TI  - Adaptive Heterogeneous Autoregressive Models of Realized Volatility Based on a Genetic Algorithm
AU  - Qu, Hui
AU  - Ji, Ping
T2  - Abstract and Applied Analysis
AB  - The heterogeneous autoregressive (HAR) models of high-frequency realized volatility are inspired by the Heterogeneous Market Hypothesis and incorporate daily, weekly and monthly realized volatilities in the volatility dynamics with a (1,5,22) time horizon structure. We build on the HAR models and propose a new framework, adaptive heterogeneous autoregressive (AHAR) models, whose time horizon structures are optimized by a genetic algorithm. Our models can be applied to markets with different heterogeneous structures, and their time horizon structures can be adjusted adaptively as the market's heterogeneous structure varies. Moving window tests with five-minute returns of the CSI 300 index indicate that the (1,5,22) structure originally proposed for American stock markets is not the best choice for Chinese stock markets, and Chinese stock markets’ heterogeneous structure does vary over time. Using four common loss functions, we find that the AHAR models outperform the corresponding HAR models in most of the forecast windows and thus are reasonable choices for volatility forecasting practices.
DA  - 2014///
PY  - 2014
DO  - 10.1155/2014/943041
DP  - DOI.org (Crossref)
VL  - 2014
SP  - 1
EP  - 8
J2  - Abstract and Applied Analysis
LA  - en
SN  - 1085-3375, 1687-0409
UR  - http://www.hindawi.com/journals/aaa/2014/943041/
Y2  - 2021/12/06/
L4  - https://downloads.hindawi.com/journals/aaa/2014/943041.pdf
ER  - 

TY  - JOUR
TI  - Forecasting financial time series volatility using Particle Swarm Optimization trained Quantile Regression Neural Network
AU  - Pradeepkumar, Dadabada
AU  - Ravi, Vadlamani
T2  - Applied Soft Computing
DA  - 2017/09//
PY  - 2017
DO  - 10.1016/j.asoc.2017.04.014
DP  - DOI.org (Crossref)
VL  - 58
SP  - 35
EP  - 52
J2  - Applied Soft Computing
LA  - en
SN  - 15684946
UR  - https://linkinghub.elsevier.com/retrieve/pii/S1568494617301862
Y2  - 2021/12/06/
ER  - 

TY  - JOUR
TI  - Exploring the predictability of range‐based volatility estimators using recurrent neural networks
AU  - Petneházi, Gábor
AU  - Gáll, József
T2  - Intelligent Systems in Accounting, Finance and Management
DA  - 2019/07//
PY  - 2019
DO  - 10.1002/isaf.1455
DP  - DOI.org (Crossref)
VL  - 26
IS  - 3
SP  - 109
EP  - 116
J2  - Intell Sys Acc Fin Mgmt
LA  - en
SN  - 1055-615X, 1099-1174
UR  - https://onlinelibrary.wiley.com/doi/10.1002/isaf.1455
Y2  - 2021/12/06/
L4  - https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/isaf.1455
ER  - 

TY  - JOUR
TI  - pth Moment stability of stochastic neural networks with Markov volatilities
AU  - Peng, Jun
AU  - Liu, Zaiming
T2  - Neural Computing and Applications
AB  - This paper is concerned with the pth moment stability of stochastic Grossberg-Hopfield neural networks with time-varying delays and Markovian volatilities. In such neural networks, the feature of stochastic systems, time-varying delay systems, and Markovian switching are taken into account. New conditions ensuring pth moment exponential stability of the considered system are presented by use of Lyapunov method and stochastic analysis theory. Finally, an example is provided to illustrate the effectiveness of the results.
DA  - 2011/06/01/
PY  - 2011
DO  - 10.1007/s00521-011-0542-5
DP  - Springer Link
VL  - 20
IS  - 4
SP  - 543
EP  - 547
J2  - Neural Comput & Applic
LA  - en
SN  - 1433-3058
UR  - https://doi.org/10.1007/s00521-011-0542-5
Y2  - 2021/12/06/
ER  - 

TY  - JOUR
TI  - Volatility estimation using support vector machine: Applications to major foreign exchange rates
AU  - Chung, Steve S.
AU  - Zhang, Serin
T2  - Electronic Journal of Applied Statistical Analysis
AB  - In finance, volatility is fundamentally important because it is associated with the risk.  A growing body of literature shows that risks associated with volatility are priced in stock, option, bond, and foreign exchange markets.  Therefore, an accurate measurement and estimation of the volatility is critical in financial markets.  The generalized autoregressive conditional heteroskedasticity (GARCH) has been one of the most popular volatility models and the model is usually estimated from the maximum likelihood estimation (MLE) method.  In this paper, we attempt to improve the MLE-based GARCH forecast using the support vector machine (SVM).   We also compare with two asymmetric volatility models:  exponential GARCH (E-GARCH) and Glosten-Jagannathan-Runkle GARCH (GJR-GARCH).  We carry out the analysis through simulations and real datasets.  The results show that the GARCH- and SVM-based volatility model provides better predictive potential than the existing volatility models.
DA  - 2017/10/14/
PY  - 2017
DP  - siba-ese.unisalento.it
VL  - 10
IS  - 2
SP  - 499
EP  - 511
LA  - en
SN  - 2070-5948
ST  - Volatility estimation using support vector machine
UR  - http://siba-ese.unisalento.it/index.php/ejasa/article/view/17080
Y2  - 2021/12/07/14:15:12
L2  - http://siba-ese.unisalento.it/index.php/ejasa/article/view/17080
ER  - 

TY  - CONF
TI  - An Empirical Study of Volatility Predictions: Stock Market Analysis Using Neural Networks
AU  - Fong, Bernard
AU  - Fong, A. C. M.
AU  - Hong, G. Y.
AU  - Wong, Louisa
A2  - Deng, Xiaotie
A2  - Ye, Yinyu
T3  - Lecture Notes in Computer Science
AB  - Volatility is one of the major factor that causes uncertainty in short term stock market movement. Empirical studies based on stock market data analysis were conducted to forecast the volatility for the implementation and evaluation of statistical models with neural network analysis. The model for prediction of Stock Exchange short term analysis uses neural networks for digital signal processing of filter bank computation. Our study shows that in the set of four stocks monitored, the model based on moving average analysis provides reasonably accurate volatility forecasts for a range of fifteen to twenty trading days.
C1  - Berlin, Heidelberg
C3  - Internet and Network Economics
DA  - 2005///
PY  - 2005
DO  - 10.1007/11600930_47
DP  - Springer Link
SP  - 473
EP  - 480
LA  - en
PB  - Springer
SN  - 978-3-540-32293-1
ST  - An Empirical Study of Volatility Predictions
KW  - Error Forecast
KW  - General Regression Neural Network
KW  - Neural Network
KW  - Probabilistic Neural Network
KW  - Stock Market
ER  - 

TY  - JOUR
TI  - Implied volatility parameterization based on a machine learning polynomial approach
AU  - Pouralizadeh, Mostafa
AU  - Badamchizadeh, Abdolrahim
AU  - Morales, Manuel
T2  - Intelligent Data Analysis
AB  - Implied volatility modeling is the future anticipation of price fluctuation and so has a crucial role in option pricing. Machine learning approach can be applied as a great tool to modeling implied volatility and predicting the corresponding future data working towards improving the validity of final outcomes. Usualy, the majority of traders and investors are willing to be encountered with a simple model which is easy to understand, so we provide a light method to reach the goal. In this paper, we propose a machine learning polynomial approach due to the smile shaped behavior of implied volatility and investigate it with a regularization penalty term to fit the Out-The-Money volatility data and we compare the result with the prominent counterpart SVI. Finally, the promising numerical results illustrate that the new proposed algorithm yields an implied volatility smile which is free from static arbitrage for Out-The-Money European call options most of the time and it outperforms SVI in prediction.
DA  - 2018/09/26/
PY  - 2018
DO  - 10.3233/IDA-173600
DP  - ResearchGate
VL  - 22
SP  - 1127
EP  - 1141
J2  - Intelligent Data Analysis
L4  - https://www.researchgate.net/profile/Mostafa-Pouralizadeh/publication/327956660_Implied_volatility_parameterization_based_on_a_machine_learning_polynomial_approach/links/5f3ebb5192851cd3020abc0d/Implied-volatility-parameterization-based-on-a-machine-learning-polynomial-approach.pdf
L4  - https://www.researchgate.net/publication/327956660_Implied_volatility_parameterization_based_on_a_machine_learning_polynomial_approach
ER  - 

TY  - JOUR
TI  - Performance evaluation of deep neural networks for forecasting time-series with multiple structural breaks and high volatility
AU  - Kaushik, Rohit
AU  - Jain, Shikhar
AU  - Jain, Siddhant
AU  - Dash, Tirtharaj
T2  - CAAI Transactions on Intelligence Technology
AB  - The problem of automatic and accurate forecasting of time-series data has always been an interesting challenge for the machine learning and forecasting community. A majority of the real-world time-series problems have non-stationary characteristics that make the understanding of trend and seasonality difficult. Our interest in this paper is to study the applicability of the popular deep neural networks (DNN) as function approximators for non-stationary TSF. We evaluate the following DNN models: Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN), and RNN with Long-Short Term Memory (LSTM-RNN) and RNN with Gated-Recurrent Unit (GRU-RNN). These DNN methods have been evaluated over 10 popular Indian financial stocks data. Further, the performance evaluation of these DNNs has been carried out in multiple independent runs for two settings of forecasting: (1) single-step forecasting, and (2) multi-step forecasting. These DNN methods show convincing performance for single-step forecasting (one-day ahead forecast). For the multi-step forecasting (multiple days ahead forecast), we have evaluated the methods for different forecast periods. The performance of these methods demonstrates that long forecast periods have an adverse effect on performance.
DA  - 2021/09//
PY  - 2021
DO  - 10.1049/cit2.12002
DP  - arXiv.org
VL  - 6
IS  - 3
SP  - 265
EP  - 280
J2  - CAAI trans. intell. technol.
SN  - 2468-2322, 2468-2322
UR  - http://arxiv.org/abs/1911.06704
Y2  - 2021/12/07/14:20:39
L2  - https://arxiv.org/abs/1911.06704
L4  - https://arxiv.org/pdf/1911.06704.pdf
N1  - <p>Comment: Preprint (18 pages)</p>
KW  - Computer Science - Machine Learning
KW  - Computer Science - Neural and Evolutionary Computing
KW  - Statistics - Machine Learning
ER  - 

TY  - BOOK
TI  - Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition
AU  - Lantz, Brett
AB  - Solve real-world data problems with R and machine learningKey FeaturesThird edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyondHarness the power of R to build flexible, effective, and transparent machine learning modelsLearn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett LantzBook DescriptionMachine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.What you will learnDiscover the origins of machine learning and how exactly a computer learns by examplePrepare your data for machine learning work with the R programming languageClassify important outcomes using nearest neighbor and Bayesian methodsPredict future events using decision trees, rules, and support vector machinesForecast numeric data and estimate financial values using regression methodsModel complex processes with artificial neural networks — the basis of deep learningAvoid bias in machine learning modelsEvaluate your models and improve their performanceConnect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlowWho this book is forData scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.
DA  - 2019/04/15/
PY  - 2019
DP  - Google Books
SP  - 459
LA  - en
PB  - Packt Publishing Ltd
SN  - 978-1-78829-155-2
ST  - Machine Learning with R
L2  - https://books.google.com.co/books?id=iNuSDwAAQBAJ
KW  - Computers / Artificial Intelligence / General
KW  - Computers / Data Science / Data Analytics
KW  - Computers / Programming / Algorithms
ER  - 

TY  - CONF
TI  - Currency options volatility forecasting with shift-invariant wavelet transform and neural networks
AU  - Liu, Fan-Yong
AU  - Liu, Fan-Xin
T3  - ICONIP'06
AB  - This paper describes four currency options volatility forecasting models. These models are based on shift-invariant wavelet transform and neural networks techniques. The à trous algorithm is used to realize the shift-invariant wavelet transform. Wavelets provide a decomposition of the volatility in a nonlinear feature space. Neural networks are used to infer future volatility from the feature space. The individual wavelet domain forecasts are recombined by different techniques to form the accurate overall forecast. The proposed models have been tested with the USD/Yen options volatility market data. Experimental results show that wavelet prediction scheme has the best forecasting performance on testing dataset among four models, with regards to the least error values. Therefore, wavelet prediction scheme outperforms the other three models and avoids effectively over-fitting problems.
C1  - Berlin, Heidelberg
C3  - Proceedings of the 13th international conference on Neural information processing - Volume Part III
DA  - 2006/10/03/
PY  - 2006
DP  - ACM Digital Library
SP  - 461
EP  - 468
PB  - Springer-Verlag
SN  - 978-3-540-46484-6
Y2  - 2021/12/07/
ER  - 

TY  - JOUR
TI  - Volatility forecasting for interbank offered rate using grey extreme learning machine: The case of China
AU  - Liu, Xiaoyong
AU  - Fu, Hui
T2  - Chaos, Solitons & Fractals
T3  - Nonlinear Dynamics and Complexity
AB  - Interbank Offered rate is the only direct market rate in China’s currency market. Volatility forecasting of China Interbank Offered Rate (IBOR) has a very important theoretical and practical significance for financial asset pricing and financial risk measure or management. However, IBOR is a dynamics and non-steady time series whose developmental changes have stronger random fluctuation, so it is difficult to forecast the volatility of IBOR. This paper offers a hybrid algorithm using grey model and extreme learning machine (ELM) to forecast volatility of IBOR. The proposed algorithm is composed of three phases. In the first, grey model is used to deal with the original IBOR time series by accumulated generating operation (AGO) and weaken the stochastic volatility in original series. And then, a forecasting model is founded by using ELM to analyze the new IBOR series. Lastly, the predictive value of the original IBOR series can be obtained by inverse accumulated generating operation (IAGO). The new model is applied to forecasting Interbank Offered Rate of China. Compared with the forecasting results of BP and classical ELM, the new model is more efficient to forecasting short- and middle-term volatility of IBOR.
DA  - 2016/08/01/
PY  - 2016
DO  - 10.1016/j.chaos.2015.11.033
DP  - ScienceDirect
VL  - 89
SP  - 249
EP  - 254
J2  - Chaos, Solitons & Fractals
LA  - en
SN  - 0960-0779
ST  - Volatility forecasting for interbank offered rate using grey extreme learning machine
UR  - https://www.sciencedirect.com/science/article/pii/S0960077915003987
Y2  - 2021/12/07/14:27:55
L2  - https://www.sciencedirect.com/science/article/abs/pii/S0960077915003987
KW  - Artificial neural network
KW  - Extreme learning machine
KW  - Grey model
KW  - Nonlinear dynamics system
KW  - Volatility forecasting
ER  - 

TY  - JOUR
TI  - An alternative approach to estimating demand: Neural network regression with conditional volatility for high frequency air passenger arrivals
AU  - Medeiros, Marcelo C.
AU  - McAleer, Michael
AU  - Slottje, Daniel
AU  - Ramos, Vicente
AU  - Rey-Maquieira, Javier
T2  - Journal of Econometrics
T3  - Estimating demand systems and measuring consumer preferences
AB  - In this paper we provide an alternative approach to analyze the demand for international tourism in the Balearic Islands, Spain, by using a neural network model that incorporates time-varying conditional volatility. We consider daily air passenger arrivals to Palma de Mallorca, Ibiza and Mahon, which are located in the islands of Mallorca, Ibiza and Menorca, respectively, as a proxy for international tourism demand for the Balearic Islands. Spain is a world leader in terms of total international tourist arrivals and receipts, and Mallorca is one of the most popular destinations in Spain. For tourism management and marketing, it is essential to forecast high frequency international tourist demand accurately. As it is important to provide sensible international tourism demand forecast intervals, it is also necessary to model their variances accurately. Moreover, time-varying variances provide useful information regarding the risks associated with variations in international tourist arrivals.
DA  - 2008/12/01/
PY  - 2008
DO  - 10.1016/j.jeconom.2008.09.018
DP  - ScienceDirect
VL  - 147
IS  - 2
SP  - 372
EP  - 383
J2  - Journal of Econometrics
LA  - en
SN  - 0304-4076
ST  - An alternative approach to estimating demand
UR  - https://www.sciencedirect.com/science/article/pii/S0304407608001516
Y2  - 2021/12/07/14:29:16
L2  - https://www.sciencedirect.com/science/article/abs/pii/S0304407608001516
KW  - Neural networks
KW  - Nonlinear models
KW  - Passenger arrivals
KW  - Semi-parametric models
KW  - Smooth transition
KW  - Time series
KW  - Tourism demand
ER  - 

TY  - JOUR
TI  - Applications of Support Vector Machine in modeling and forecasting stock market volatility
AU  - Ou, Phichhang
AU  - Wang, Hengshan
T2  - International Information Institute (Tokyo). Information
DA  - 2012///
PY  - 2012
VL  - 15
IS  - 8
SP  - 3365
J2  - International Information Institute (Tokyo). Information
SN  - 1343-4500
ER  - 

TY  - JOUR
TI  - Volatility Modelling and Prediction by Hybrid Support Vector Regression with Chaotic Genetic Algorithms
AU  - Ou, Phichhang
AU  - Wang, Hengshan
T2  - International Arab Journal of Information Technology
AB  - In this paper, a new econometric model of volatility is proposed using hybrid Support Vector machine for Regression (SVR) combined with Chaotic Genetic Algorithm (CGA) to fit conditional mean and then conditional variance of stock market returns. The CGA, integrated by chaotic optimization algorithm with Genetic Algorithm (GA), is used to overcome premature local optimum in determining three hyperparameters of SVR model. The proposed hybrid SVRCGA model is achieved, which includes the selection of input variables by ARMA approach for fitting both mean and variance functions of returns, and also the searching process of obtaining the optimal SVR hyperparameters based on the CGA while training the SVR. Real data of complex stock markets (NASDAQ) are applied to validate and check the predicting accuracy of the hybrid SVRCGA model. The experimental results showed that the proposed model outperforms the other competing models including SVR with GA, standard SVR, Kernel smoothing and several parametric GARCH type models.
DA  - 2014/05/01/
PY  - 2014
DP  - ResearchGate
VL  - 11
SP  - 287
EP  - 292
J2  - International Arab Journal of Information Technology
L4  - https://www.researchgate.net/publication/287401243_Volatility_Modelling_and_Prediction_by_Hybrid_Support_Vector_Regression_with_Chaotic_Genetic_Algorithms
ER  - 

TY  - JOUR
TI  - Applied machine learning and management of volatility, uncertainty, complexity & ambiguity (VUCA).
AU  - Patnaik, Srikanta
T2  - Journal of Intelligent & Fuzzy Systems
DA  - 2020///
PY  - 2020
VL  - 39
IS  - 2
SP  - 1
EP  - 8
J2  - Journal of Intelligent & Fuzzy Systems
ER  - 

TY  - JOUR
TI  - Delta-neutral volatility trading using neural networks
AU  - Calôba, L.O.M.
AU  - Calôba, L.P.
AU  - Contador, C.R.
T2  - International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications
AB  - In this paper we propose a methodology to forecast the daily changes of the market volatility (ISD) using neural networks. We define the input variables based on specific literature but using lag and horizon of the training set calculated so that we guarantee the significancy of its correlation with the output variable through time. We use this methodology on data from Telebrás PN Stock options from August 1994 through November 1996. Then, based on out-of-sample projections of the model we simulate a trading volatility strategy, creating Delta-hedged portfolios that result in abnormal returns in the market.
DA  - 2001/12/01/
PY  - 2001
DP  - ResearchGate
VL  - 9
SP  - 243
EP  - 249
J2  - International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications
L4  - https://www.researchgate.net/publication/295758036_Delta-neutral_volatility_trading_using_neural_networks
ER  - 

TY  - CHAP
TI  - Machine Learning for Health Informatics
AU  - Holzinger, Andreas
T2  - Machine Learning for Health Informatics: State-of-the-Art and Future Challenges
AB  - Machine Learning (ML) studies algorithms which can learn from data to gain knowledge from experience and to make decisions and predictions. Health Informatics (HI) studies the effective use of probabilistic information for decision making. The combination of both has greatest potential to rise quality, efficacy and efficiency of treatment and care. Health systems worldwide are confronted with "big data" in high dimensions, where the inclusion of a human is impossible and automatic ML (aML) show impressive results. However, sometimes we are confronted with complex data, "little data", or rare events, where aML-approaches suffer of insufficient training samples. Here interactive ML (iML) may be of help, particularly with a doctor-in-the-loop, e.g. in subspace clustering, k-Anonymization, protein folding and protein design. However, successful application of ML for HI needs an integrated approach, fostering a concerted effort of four areas: (1) data science, (2) algorithms (with focus on networks and topology (structure), and entropy (time), (3) data visualization, and last but not least (4) privacy, data protection, safety & security.
CY  - Cham
DA  - 2016///
PY  - 2016
DP  - Web of Science Nextgen
VL  - 9605
SP  - 1
EP  - 24
LA  - English
PB  - Springer International Publishing Ag
SN  - 978-3-319-50478-0 978-3-319-50477-3
UR  - http://www.webofscience.com/wos/woscc/full-record/WOS:000408904100002
Y2  - 2021/12/13/18:12:39
KW  - classification
KW  - consensus
KW  - entropy
KW  - future
KW  - Health informatics
KW  - knowledge discovery
KW  - Machine learning
KW  - of-the-art
ER  - 

TY  - JOUR
TI  - Two improved k-means algorithms
AU  - Yu, Shyr-Shen
AU  - Chu, Shao-Wei
AU  - Wang, Chuin-Mu
AU  - Chan, Yung-Kuan
AU  - Chang, Ting-Cheng
T2  - Applied Soft Computing
AB  - K-means algorithm is the most commonly used simple clustering method. For a large number of high dimensional numerical data, it provides an efficient method for classifying similar data into the same cluster. In this study, a tri-level k-means algorithm and a bi-layer k-means algorithm are proposed. The k-means algorithm is vulnerable to outliers and noisy data, and also susceptible to initial cluster centers. The tri-level k-means algorithm can overcome these drawbacks. While the data in a dataset S are often changed, after a period of time the trained cluster centers cannot precisely describe the data in each cluster. The cluster centers hence need to be updated. In this paper, an online machine learning based tri-level k-means algorithm is also provided to solve this problem. When the data in a cluster are significantly different, a cluster center cannot alone precisely describe each datum in the cluster. Noisy data, outliers, and data with quite different values in the same cluster may decrease the performance of pattern matching systems. The bi-layer k-means algorithm can deal with the above problems. Meanwhile, a genetic-based algorithm is provided to derive the fittest parameters used in the tri-level and bi-layer k-means algorithms. Experimental results demonstrate that both algorithms can provide much better accuracy of classification than the traditional k-means algorithm. (C) 2017 Elsevier B.V. All rights reserved.
DA  - 2018/07//
PY  - 2018
DO  - 10.1016/j.asoc.2017.08.032
DP  - Web of Science Nextgen
VL  - 68
SP  - 747
EP  - 755
J2  - Appl. Soft. Comput.
LA  - English
SN  - 1568-4946
UR  - http://www.webofscience.com/wos/woscc/full-record/WOS:000433155300052
Y2  - 2021/12/13/18:16:50
KW  - classified vector quantization
KW  - Genetic algorithm
KW  - k-Means algorithm
KW  - Noise   data
KW  - Online machine learning
KW  - Outlier
ER  - 

TY  - JOUR
TI  - An overview on semi-supervised support vector machine
AU  - Ding, Shifei
AU  - Zhu, Zhibin
AU  - Zhang, Xiekai
T2  - Neural Computing & Applications
AB  - Support vector machine (SVM) is a machine learning method based on statistical learning theory. It has a lot of advantages, such as solid theoretical foundation, global optimization, the sparsity of the solution, nonlinear and generalization. The standard form of SVM only applies to supervised learning. Large amount of data generated in real life is unlabeled, and the standard form of SVM cannot make good use of these data to improve its learning ability. However, semi-supervised support vector machine (S3VM) is a good solution to this problem. This paper reviews the recent progress in semi-supervised support vector machine. First, the basic theory of S3VM is expounded and discussed in detail; then, the mainstream model of S3VM is presented, including transductive support vector machine, Laplacian support vector machine, S3VM training via the label mean, S3VM based on cluster kernel; finally, we give the conclusions and look ahead to the research on S3VM.
DA  - 2017/05//
PY  - 2017
DO  - 10.1007/s00521-015-2113-7
DP  - Web of Science Nextgen
VL  - 28
IS  - 5
SP  - 969
EP  - 978
J2  - Neural Comput. Appl.
LA  - English
SN  - 0941-0643
UR  - http://www.webofscience.com/wos/woscc/full-record/WOS:000401342700010
Y2  - 2021/12/13/18:18:24
KW  - algorithm
KW  - framework
KW  - image classification
KW  - kernel
KW  - manifold regularization
KW  - Semi-supervised
KW  - Semi-supervised support vector   machine
KW  - Support vector machine
ER  - 

TY  - JOUR
TI  - Deep Learning
AU  - Hao, Xing
AU  - Zhang, Guigang
AU  - Ma, Shang
T2  - International Journal of Semantic Computing
AB  - Deep learning is a branch of machine learning that tries to model high-level abstractions of data using multiple layers of neurons consisting of complex structures or non-liner transformations. With the increase of the amount of data and the power of computation, neural networks with more complex structures have attracted widespread attention and been applied to various fields. This paper provides an overview of deep learning in neural networks including popular architecture models and training algorithms.
DA  - 2016/09//
PY  - 2016
DO  - 10.1142/S1793351X16500045
DP  - Web of Science Nextgen
VL  - 10
IS  - 3
SP  - 417
EP  - 439
J2  - Int. J. Semant. Comput.
LA  - English
SN  - 1793-351X
UR  - http://www.webofscience.com/wos/woscc/full-record/WOS:000389655900008
Y2  - 2021/12/13/18:19:12
KW  - Deep learning
KW  - networks
KW  - neural networks
KW  - training
ER  - 

TY  - CHAP
TI  - Recurrent neural networks are universal approximators
AU  - Schaefer, Anton Maximilian
AU  - Zimmermann, Hans Georg
T2  - Artificial Neural Networks - Icann 2006, Pt 1
A2  - Kollias, S.
A2  - Stafylopatis, A.
A2  - Duch, W.
A2  - Oja, E.
AB  - Neural networks represent a class of functions for the efficient identification and forecasting of dynamical systems. It has been shown that feedforward networks are able to approximate any (Borel-)measurable function on a compact domain [1,2,3]. Recurrent neural networks (RNNs) have been developed for a better understanding and analysis of open dynamical systems. Compared to feedforward networks they have several advantages which have been discussed extensively in several papers and books, e.g. [4]. Still the question often arises if RNNs are able to map every open dynamical system, which would be desirable for a broad spectrum of applications. In this paper we give a proof for the universal approximation ability of RNNs in state space model form. The proof is based on the work of Hornik, Stinchcombe, and White about feedforward neural networks [1].
CY  - Berlin
DA  - 2006///
PY  - 2006
DP  - Web of Science Nextgen
VL  - 4131
SP  - 632
EP  - 640
LA  - English
PB  - Springer-Verlag Berlin
SN  - 978-3-540-38625-4
UR  - http://www.webofscience.com/wos/woscc/full-record/WOS:000241472100066
Y2  - 2021/12/13/18:23:16
ER  - 

TY  - JOUR
TI  - Big data in medicine and healthcare
AU  - Rueping, Stefan
T2  - Bundesgesundheitsblatt-Gesundheitsforschung-Gesundheitsschutz
AB  - Healthcare is one of the business fields with the highest Big Data potential. According to the prevailing definition, Big Data refers to the fact that data today is often too large and heterogeneous and changes too quickly to be stored, processed, and transformed into value by previous technologies. The technological trends drive Big Data: business processes are more and more executed electronically, consumers produce more and more data themselves - e.g. in social networks - and finally ever increasing digitalization. Currently, several new trends towards new data sources and innovative data analysis appear in medicine and healthcare. From the research perspective, omics-research is one clear Big Data topic. In practice, the electronic health records, free open data and the "quantified self" offer new perspectives for data analytics. Regarding analytics, significant advances have been made in the information extraction from text data, which unlocks a lot of data from clinical documentation for analytics purposes. At the same time, medicine and healthcare is lagging behind in the adoption of Big Data approaches. This can be traced to particular problems regarding data complexity and organizational, legal, and ethical challenges. The growing uptake of Big Data in general and first best-practice examples in medicine and healthcare in particular, indicate that innovative solutions will be coming. This paper gives an overview of the potentials of Big Data in medicine and healthcare.
DA  - 2015/08//
PY  - 2015
DO  - 10.1007/s00103-015-2181-y
DP  - Web of Science Nextgen
VL  - 58
IS  - 8
SP  - 794
EP  - 798
J2  - Bundesgesundheitsblatt-Gesund.
LA  - German
SN  - 1436-9990
UR  - http://www.webofscience.com/wos/woscc/full-record/WOS:000358598500003
Y2  - 2021/12/13/18:33:16
KW  - Analytics
KW  - Big Data
KW  - Data Mining
KW  - Data Science
ER  - 

TY  - JOUR
TI  - Quantum Genetic Algorithms for Computer Scientists
AU  - Lahoz-Beltra, Rafael
T2  - Computers
AB  - Genetic algorithms (GAs) are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical processes such as reproduction, selection, etc. Over the last decade, the possibility to emulate a quantum computer (a computer using quantum-mechanical phenomena to perform operations on data) has led to a new class of GAs known as "Quantum Genetic Algorithms" (QGAs). In this review, we present a discussion, future potential, pros and cons of this new class of GAs. The review will be oriented towards computer scientists interested in QGAs "avoiding" the possible difficulties of quantum-mechanical phenomena.
DA  - 2016/12//
PY  - 2016
DO  - 10.3390/computers5040024
DP  - Web of Science Nextgen
VL  - 5
IS  - 4
SP  - 24
J2  - Computers
LA  - English
SN  - 2073-431X
UR  - http://www.webofscience.com/wos/woscc/full-record/WOS:000390875100005
Y2  - 2021/12/13/18:42:24
L1  - https://www.mdpi.com/2073-431X/5/4/24/pdf
KW  - evolutionary algorithm
KW  - gate
KW  - quantum computing
KW  - quantum evolutionary algorithms
KW  - quantum genetic algorithms
KW  - reduced   quantum genetic algorithm
ER  - 

TY  - JOUR
TI  - Artificial intelligence
AU  - Scotti, Veronica
T2  - Ieee Instrumentation & Measurement Magazine
AB  - Recently, we have observed an impressive evolution in the field of Artificial Intelligence (AI) which aims to reach any area of human activities to reduce the efforts of people related to boring daily jobs. AI is defined as the capacity of a computer to perform tasks commonly associated with human beings.
DA  - 2020/05//
PY  - 2020
DO  - 10.1109/MIM.2020.9082795
DP  - Web of Science Nextgen
VL  - 23
IS  - 3
SP  - 27
EP  - 31
J2  - IEEE Instrum. Meas. Mag.
LA  - English
SN  - 1094-6969
UR  - http://www.webofscience.com/wos/woscc/full-record/WOS:000532226700005
Y2  - 2021/12/13/18:48:13
KW  - Artificial intelligence
KW  - Law
KW  - Patents
KW  - Robots
KW  - Task analysis
ER  -