Elements of artificial neural networks
Elements of artificial neural networks
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Evolving RBF neural networks for time-series forecasting with EvRBF
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Comparing classification techniques for predicting essential hypertension
Expert Systems with Applications: An International Journal
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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.