Volatility Forecasting using Time Series Data Mining and Evolutionary Computation Techniques

被引：0

作者：

Ma, Irwin ^{[1
]}

Wong, Tony ^{[1
]}

Sankar, Thiagas ^{[1
]}

机构：

[1] Ecole Technol Super, Montreal, PQ H3C 1K3, Canada

来源：

GECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2 | 2007年

关键词：

Financial Volatility; forecasting; genetic algorithm; genetic programming; data mining; S&P 100;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Traditional parametric methods have limited success in estimating and forecasting the volatility of financial securities. Recent advance in evolutionary computation has provided additional tools to conduct data mining effectively. The Current work applies the genetic programming in a Time Series Data Mining framework to characterize the S&P100 high frequency data in order to forecast the one step ahead integrated volatility. Results of the experiment have shown to be superior to those derived by the traditional methods.

引用

页码：2262 / 2262

页数：1

共 50 条

[21] Forecasting sea level changes applying data mining techniques to the Cristobal Bay time series, Panama
Simmonds, Jose A.
Gomez, Juan A.
Ledezma, Agapito
[J]. JOURNAL OF WATER AND CLIMATE CHANGE, 2017, 8 (01) : 89 - 101
[22] Time Series Forecasting With Volatility Activation Function
Kayim, Furkan
Yilmaz, Atinc
[J]. IEEE ACCESS, 2022, 10 : 104000 - 104010
[23] Performance comparison and future estimation of time series data using predictive data mining techniques
Tanwar, Harshita
Kakkar, Misha
[J]. 2017 1ST IEEE INTERNATIONAL CONFERENCE ON DATA MANAGEMENT, ANALYTICS AND INNOVATION (ICDMAI), 2017, : 9 - 12
[24] Time Series Data Mining for Energy Prices Forecasting: An Application to Real Data
Costa E Silva, Eliana
Borges, Ana
Filomena Teodoro, M.
Andrade, Marina A. P.
Covas, Ricardo
[J]. INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS (ISDA 2016), 2017, 557 : 649 - 658
[25] Effective probability forecasting for time series data using standard machine learning techniques
Lindsay, D
Cox, S
[J]. PATTERN RECOGNITION AND DATA MINING, PT 1, PROCEEDINGS, 2005, 3686 : 35 - 44
[26] An approach for runoff computation using three data mining techniques
Bojkov, V
[J]. URBAN WATER MANAGEMENT: SCIENCE TECHNOLOGY AND SERVICE DELIVERY, 2003, 25 : 135 - 148
[27] Electric Power Price Forecasting Using Data Mining Techniques
Patil, Mrinal
Deshmukh, Surekha R.
Agrawal, Ritu
[J]. 2017 1ST IEEE INTERNATIONAL CONFERENCE ON DATA MANAGEMENT, ANALYTICS AND INNOVATION (ICDMAI), 2017, : 217 - 223
[28] Data mining in load forecasting: An approach using fuzzy techniques
Lambert-Torres, G.
Marra, W.
Lage, W. F.
de Moraes, C. H. V.
Costa, C. I. A.
[J]. 2006 POWER ENGINEERING SOCIETY GENERAL MEETING, VOLS 1-9, 2006, : 3430 - +
[29] Improving Multiple Time Series Forecasting with Data Stream Mining Algorithms
Mochinski, Marcos Alberto
Barddal, Jean Paul
Enembreck, Fabricio
[J]. 2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2020, : 1060 - 1067
[30] Evolutionary collective behavior decomposition model for time series data mining
Qin, Zengchang
Wan, Tao
Dong, Yingsai
Du, Yu
[J]. APPLIED SOFT COMPUTING, 2015, 26 : 368 - 377

← 1 2 3 4 5 →