Journal of Computations & Modelling

Applying Neural Network for the Improvement of Forecasting Accuracy –Airlines Weekly Cargo Data Case–

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  • Abstract

     

    In recent years, severe competition is executed both on getting air passengers and those of air cargos. The forecast of the number of taking-off and landing is expanding, while demand for air cargo is decreasing. Strict marketing is required in such fields. Forecasting the trend of air cargo is an essential item to be investigated in airlines. In order to make forecast for time series, the method of using linear model is often used. Forecasting using neural network is also developed. Reviewing past researches, there are many researches made on this. There is many room to improve in neural network, therefore we make focus on them. We use time series data, and in order to make forecast, a new coming data should be handled and the parameter should be estimated based upon its data. This is a so-called on-line parameter estimation. In this paper, neural network is applied and Multilayer perceptron Algorithm is newly developed. The method is applied to the Airlines Cargo Data in the case of Weekly data. When the data do not behave regularly, we have to device a new method for the neural networks to learn the past data much more. Repeating the data into plural sections, we could make a neural network to learn the past data much more. The result is compared with the method of ARIMA model. Good results were obtained. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.