Forecasting time series data using hybrid grey relational artificial neural network and auto regressive integrated moving average model

• Main Authors: Salleh, Roselina, Shamsuddin, Siti Mariyam
• Format: Book Section
• Language: English
• Published: ICS AS 2007
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/9635/1/RoselinaSalleh2007_ForecastingTimeSeriesDataUsing.pdf; http://eprints.utm.my/id/eprint/9635/
• Institution: Universiti Teknologi Malaysia

In business, industry and government agencies, anticipating future behavior that involves many critical variables for nation wealth creation is vitally important, thus the necessity to make precise decision by the policy makers is really essential. Consequently, an accurate and reliable forecast system is needed to compose such predictions. Accordingly, the aim of this research is to develop a new hybrid model by combining a linear and nonlinear model for forecasting time series data. The proposed model (GRANN_ARIMA) integrates nonlinear grey relational articial neural network (GRANN) and linear ARIMA model, combining new features such as multivariate time series data as well as grey relational analysis to select the appropriate inputs and hybridization succession. To validate the performance of the proposed model, small and large scale data sets are used. The forecasting performance was compared with several models, and these include: individual models (ARIMA, multiple regression, grey relational articial neural network), several hybrid models (MARMA, MR_ANN, ARIMA_ANN), and articial neural network (ANN) trained using levenberg marquardt algorithm. The experiments have shown that the proposed model has outperformed other models with 99.5% forecasting accuracy for small-scale data and 99.84% for large-scale data. The empirical results obtained have proved that the GRANN_ARIMA model can provide a better alternative for time series forecasting due to its promising performance and capability in handling time series data for both small and large scale data.