Time series forecasting based on wavelet filtering

Tae Woo Joo, Seoung Bum Kim

Research output: Contribution to journalArticlepeer-review

73 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)3868-3874
Number of pages7
JournalExpert Systems With Applications
Issue number8
Publication statusPublished - 2015 May 15

Bibliographical note

Funding Information:
The authors sincerely thank the referees for their helpful suggestions and comments, which greatly improved the quality of the paper. This research was supported by This research was supported by Brain Korea PLUS and Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning (2013007724).

Publisher Copyright:
© 2015 Elsevier Ltd.


  • Forecasting
  • Keywords ARIMA
  • Time series
  • Wavelet transforms

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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