Home > Archive > 2014 > Volume 4 Number 6 (Dec. 2014) >
IJMLC 2014 Vol. 4(6): 491-495 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V6.460

Predicting Time Series Using Integration of Moving Average and Support Vector Regression

Chihli Hung, Chih-Neng Hung, and Szu-Yin Lin

Abstract—Time series prediction is one of the major tasks in the field of data mining. The approaches of time series prediction can be divided into statistical techniques and computational intelligence techniques. Most researchers use one specific approach and compare the performance with other approaches. This paper proposes a novel hybrid approach, which integrates traditional moving average models with support vector regression for the prediction of ATM withdrawals in England. The use of moving average modeling is not only for the purpose of smoothing but also for time series prediction. We treat a weekly median moving average as the benchmark. Based on experimental results, our proposed approach consistently outperforms the benchmark.

Index Terms—Support vector regression, cash withdrawal analysis, time series prediction, data mining.

Chihli Hung and Szu-Yin Lin are with the Department of Information Management, Chung Yuan Christian University, Chungli 32023, Taiwan, ROC (e-mail: chihli@cycu.edu.tw, stan@cycu.edu.tw).
Chih-Neng Hung is with the Department of Information and Technology Education, Ministry of Education, Taipei, Taiwan, ROC (e-mail: hcn0619@gmail.com).

[PDF]

Cite: Chihli Hung, Chih-Neng Hung, and Szu-Yin Lin, "Predicting Time Series Using Integration of Moving Average and Support Vector Regression," International Journal of Machine Learning and Computing vol. 4, no. 6, pp. 491-495, 2014.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


Article Metrics