Universiti Teknologi Malaysia Institutional Repository

Forecasting Malaysia load using a hybrid model

Mohamed, Norizan and Ahmad, Maizah Hura (2010) Forecasting Malaysia load using a hybrid model. Jurnal STATISTIKA, 10 (1). pp. 1-8. ISSN 1411-5891

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A hybrid model, which combines the seasonal time series ARIMA (SARIMA) and the multilayer feed-forward neural network to forecast time series with seasonality, is shown to outperform both two single models. Besides the selection of transfer functions, the determination of hidden nodes to use for the non linear model is believed to improve the accuracy of the hybrid model. In this paper, we focus on the selection of the appropriate number of hidden nodes on the non linear model to forecast Malaysia load. Results show that by using only one hidden node, the hybrid model of Malaysia load performs better than both single models with mean absolute percentage error (MAPE) of less than 1%.

Item Type:Article
Subjects:Q Science > QA Mathematics
ID Code:25946
Deposited By: Liza Porijo
Deposited On:18 Jun 2012 11:23
Last Modified:13 Oct 2017 20:27

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