Hybrid grey relational artificial neural network and auto regressive integrated moving average model for forecasting time-series data

Abstract

The aim of this study 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 artificial neural network (GRANN) and a linear autoregressive integrated moving average (ARIMA) model by combining new features and 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 is compared with several models, and these include: individual models (ARIMA, multiple regression, GRANN), several hybrid models (MARMA, MR-ANN, ARIMA-ANN), and an artificial neural network (ANN) trained using a 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 obtained empirical results have proven 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.