Hybridization Model of Linear and Nonlinear Time Series Data for Forecasting

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 importantness and consequence on the generated output.