Abdollahian, M. ; Foroughi, Roya (2005)
Optimal Statistical Model for Forecasting Ozone.
IEEE International Conference on Information Technology: Coding and Computing 2005. Proceedings Vol. I.
Conference or Workshop Item, Bibliographie
Abstract
The objective of this paper is to apply time series analysis to Ozone data in order to obtain the optimal forecasting model . Different ARMA models are fitted to the Ozone data and the best fitted model, ARMA (20,2), is found to produce the best predictions with MAPE = 42. Applying simple exponential smoothing to the time series, however, results in even higher accuracy for predictions. This leads us to believe that in certain cases depending on the characteristics of the time series, naïve methods of forecasting may produce more accurate results.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2005 |
| Creators: | Abdollahian, M. ; Foroughi, Roya |
| Type of entry: | Bibliographie |
| Title: | Optimal Statistical Model for Forecasting Ozone |
| Language: | English |
| Date: | 2005 |
| Place of Publication: | Los Alamitos, Calif. [u.a.] |
| Publisher: | IEEE Computer Society |
| Event Title: | IEEE International Conference on Information Technology: Coding and Computing 2005. Proceedings Vol. I |
| Abstract: | The objective of this paper is to apply time series analysis to Ozone data in order to obtain the optimal forecasting model . Different ARMA models are fitted to the Ozone data and the best fitted model, ARMA (20,2), is found to produce the best predictions with MAPE = 42. Applying simple exponential smoothing to the time series, however, results in even higher accuracy for predictions. This leads us to believe that in certain cases depending on the characteristics of the time series, naïve methods of forecasting may produce more accurate results. |
| Uncontrolled Keywords: | Time series analysis, Forecasting theory, Statistics |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Interactive Graphics Systems |
| Date Deposited: | 16 Apr 2018 09:04 |
| Last Modified: | 23 Apr 2020 07:55 |
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