Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 42, Issue 8, Pages 3868-3874Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2015.01.026
Keywords
ARIMA; Forecasting; Time series; Wavelet transforms
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Funding
- Brain Korea PLUS
- National Research Foundation of Korea - Ministry of Science, ICT and Future Planning [2013007724]
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Forecasting time series data is one of the most important issues involved in numerous applications in real life. Time series data have been analyzed in either the time or frequency domains. The objective of this study is to propose a forecasting method based on wavelet filtering. The proposed method decomposes the original time series into the trend and variation parts and constructs a separate model for each part. Simulation and real case studies were conducted to examine the properties of the proposed method under various scenarios and compare its performance with time series forecasting models without wavelet filtering. The results from both simulated and real data showed that the proposed method based on wavelet filtering yielded more accurate results than the models without wavelet filtering in terms of mean absolute percentage error criterion. (C) 2015 Elsevier Ltd. All rights reserved.
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