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: firstname.lastname@example.org, email@example.com).
Chih-Neng Hung is with the Department of Information and Technology Education, Ministry of Education, Taipei, Taiwan, ROC (e-mail: firstname.lastname@example.org).
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.