A novel modified particle swarm optimization for forecasting financial time series

An-Pin Chen*, Chien Hsun Huang, Yu Chia Hsu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Time series has been widely applied in the real world; traditional methods can hardly solve the dynamic environment issue resulting from the assumption of stationary process. Many traditional models and artificial intelligence technologies had been developed under this assumption, and adapted the dynamic environment based on the time-varying characteristic. But these models still has drawback of dividing the time series into training set and testing set when developing the models. It means the time-varying characteristic of these two sets did not be considered, and it might cause spurious regression phenomenon and result in misleading the statistic analysis. In order to forecast dynamic time series, a model which can consider the dynamic environment and conquer the out-of-sample problem is necessary. Particle swarm optimization (PSO) has the characteristics of fast-convergence and avoiding local optimal, also has been widely used in the time series forecasting. In this research, we proposed a modified PSO to consider the dynamic environment issue and use the advantage of PSO to forecast the dynamic financial time series.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Pages683-687
Number of pages5
DOIs
StatePublished - 1 Dec 2009
Event2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009 - Shanghai, China
Duration: 20 Nov 200922 Nov 2009

Publication series

NameProceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Volume1

Conference

Conference2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
CountryChina
CityShanghai
Period20/11/0922/11/09

Keywords

  • Out-of-sample forecast
  • Particle swarm optimization
  • Time series forecasting

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  • Cite this

    Chen, A-P., Huang, C. H., & Hsu, Y. C. (2009). A novel modified particle swarm optimization for forecasting financial time series. In Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009 (pp. 683-687). [5357771] (Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009; Vol. 1). https://doi.org/10.1109/ICICISYS.2009.5357771