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A Hybrid Method to Improve Forecasting Accuracy - An Introduction of a Day of the Week Index for Air Cargo Weight Data

Author(s): Tatsuhiro Kuroda | Keiko Nagata | Kazuhiro Takeyasu

Journal: Journal of Computations & Modelling
ISSN 1792-7625

Volume: 2;
Issue: 3;
Start page: 89;
Date: 2012;
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Keywords: Minimum Variance | Exponential Smoothing Method | Forecasting

Air cargo loading weight forecasting is an important factor for managers in the aviation industry because revenue is dependent on the amount of weight loaded. In this paper, we propose a new method to improve forecasting accuracy and confirm them by the numerical example. Focusing that the equation of exponential smoothing method(ESM) is equivalent to (1,1) order ARMA model equation, a new method of estimation of smoothing constant in exponential smoothing method is proposed before by us which satisfies minimum variance of forecasting error. Generally, smoothing constant is selected arbitrarily. But in this paper, we utilize above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate smoothing constants. Thus theoretical solution is derived in a simple way and it may be utilized in various fields. Combining the trend removing method with this method, we aim to improve forecasting accuracy. Furthermore, “a day of the week index” is newly introduced for the daily air cargo weight data and we have obtained good result. The effectiveness of this method should be examined in various cases.
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