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 |
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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 | |
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 |
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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