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Hybridization model of linear and nonlinear time series data for forecasting

Sallehuddin, Roselina and Shamsuddin, Siti Mariyam and Mohd Hashim, Siti Zaiton (2008) Hybridization model of linear and nonlinear time series data for forecasting. In: Proceedings - 2nd Asia International Conference on Modelling and Simulation, AMS 2008. Institute of Electrical and Electronics Engineers, New York, 597 -602. ISBN 978-076953136-6

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Official URL: http://dx.doi.org/10.1109/AMS.2008.142

Abstract

The aim of this paper is to propose a novel approach in hybridizing linear and nonlinear model by incorporating several new features. The intended features are multivariate information, hybridization succession alteration, and cooperative feature selection. To assess the performance of the proposed hybrid model allegedly known as Grey Relational Artificial Neural Network(GRANN_ARIMA), extensive comparisons are done with individual model (Artificial Neural Network(ANN), Autoregressive integrated Moving Average(ARIMA) and Multiple Linear Regression(MR)) and conventional hybrid model (ARIMA_ANN) with Root Mean Square Error(RMSE), Mean Absolute Deviation(MAD), Mean Absolute Percentage Error (MAPE) and Mean Square error( MSE ). The experiments have shown that the proposed hybrid model has outperformed other models with 99.5% forecasting accuracy for small-scale data and 99.84%for large-scale data. The obtained empirical results have also proved that the GRANN-ARIMA is more accurate and robust due to its promising performance and capability in handling small and large scale time series data. In addition, the implementation of cooperative feature selection has assisted the forecaster to automatically determine the optimum number of input factor amid with its important ness and consequence on the generated output.

Item Type:Book Section
Additional Information:ISBN: 978-076953136-6; 2nd Asia International Conference on Modelling and Simulation, AMS 2008; Kuala Lumpur; 13 May 2008 through 15 May 2008
Uncontrolled Keywords:ARIMA_ANN, cooperative feature selection, forecasting, GRANN_ARIMA, hybrid
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:12583
Deposited By: Liza Porijo
Deposited On:14 Jun 2011 08:26
Last Modified:14 Jun 2011 08:26

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