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Optimal Statistical Model for Forecasting Ozone

Abdollahian, M. and Foroughi, Roya (2005):
Optimal Statistical Model for Forecasting Ozone.
IEEE Computer Society, Los Alamitos, Calif., In: IEEE International Conference on Information Technology: Coding and Computing 2005. Proceedings Vol. I, pp. 169-173, [Conference or Workshop Item]

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. and Foroughi, Roya
Title: Optimal Statistical Model for Forecasting Ozone
Language: English
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.

Publisher: IEEE Computer Society, Los Alamitos, Calif.
Uncontrolled Keywords: Time series analysis, Forecasting theory, Statistics
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: IEEE International Conference on Information Technology: Coding and Computing 2005. Proceedings Vol. I
Date Deposited: 16 Apr 2018 09:04
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