Various Type of Wavelet Filters on Time Series Forecasting

Keun Tae Park; Jun-Geol Baek

doi:10.1109/ICSC.2017.17

Various Type of Wavelet Filters on Time Series Forecasting

Keun Tae Park, Jun-Geol Baek

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 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 language	English
Title of host publication	Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	258-259
Number of pages	2
ISBN (Electronic)	9781509048960
DOIs	https://doi.org/10.1109/ICSC.2017.17
Publication status	Published - 2017 Mar 29
Event	11th IEEE International Conference on Semantic Computing, ICSC 2017 - San Diego, United States Duration: 2017 Jan 30 → 2017 Feb 1

Other

Other	11th IEEE International Conference on Semantic Computing, ICSC 2017
Country/Territory	United States
City	San Diego
Period	17/1/30 → 17/2/1

Keywords

ARIMA
Daubechies wavelet
Forecasting
Time-series
Wavelet transforms

ASJC Scopus subject areas

Computer Science Applications
Information Systems
Computer Networks and Communications

Access to Document

10.1109/ICSC.2017.17

Cite this

Park, KT & Baek, J-G 2017, Various Type of Wavelet Filters on Time Series Forecasting. in Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017., 7889536, Institute of Electrical and Electronics Engineers Inc., pp. 258-259, 11th IEEE International Conference on Semantic Computing, ICSC 2017, San Diego, United States, 17/1/30. https://doi.org/10.1109/ICSC.2017.17

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AB - 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.

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ER -

Various Type of Wavelet Filters on Time Series Forecasting

Abstract

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Keywords

ASJC Scopus subject areas

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