A Comparison of Neural Network Methods and Box-Jenkins Model in Time Series Analysis

O.H. Choon and J.L.T. Chuin (Malaysia)


Time series, neural network, multi-layered perceptron, forecasting


Many neural network methods had been proposed and applied in time series forecasting since the past decade. To gain a better understanding a comparison is made with the more conventional method in time series by describing the advantages and disadvantages of using neural networks. The time series prediction capabilities of the multi-layered perceptron neural network model (MLP) and the time series Box-Jenkins model (SARIMA) are also compared in this paper. The data used for analyzing and forecasting is a 10 years of time series (January 1996 – December 2005) monthly record for temperature data, in Bayan Lepas, Penang, Malaysia. To show the effectiveness of prior data processing, four sets of MLP simulation programs are used: the original data (O), linear detrending model (DTL), deseasonalized model (DS) and both detrending and deseasonalized model (DSTL). Results show that the neural network methods with prior data processing in time series forecasting perform better compared to the Box-Jenkins method.

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