Various Type of Wavelet Filters on Time Series Forecasting

Keun Tae Park, Jun-Geol Baek

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

2 Citations (Scopus)

Abstract

Forecasting time series data is one of the most important subjects that is useful and applicable in real life. The objective of this study improves the performances of time series forecasting method called ARIMA with wavelet transform. The proposed method is taking an optimal type of Daubechies wavelet transform functions. Real case datasets in existing paper are used to compare the performance with original and existing forecasting methods. The results of experiment demonstrate the usefulness and superiority of the proposed method with more possibility.

Original languageEnglish
Title of host publicationProceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages258-259
Number of pages2
ISBN (Electronic)9781509048960
DOIs
Publication statusPublished - 2017 Mar 29
Event11th IEEE International Conference on Semantic Computing, ICSC 2017 - San Diego, United States
Duration: 2017 Jan 302017 Feb 1

Other

Other11th IEEE International Conference on Semantic Computing, ICSC 2017
CountryUnited States
CitySan Diego
Period17/1/3017/2/1

Keywords

  • ARIMA
  • Daubechies wavelet
  • Forecasting
  • Time-series
  • Wavelet transforms

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Computer Networks and Communications

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  • Cite this

    Park, K. T., & Baek, J-G. (2017). Various Type of Wavelet Filters on Time Series Forecasting. In Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017 (pp. 258-259). [7889536] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSC.2017.17