Development of Hybrid Models for Forecasting Time-Series Data Using Nonlinear SVR Enhanced by PSO

Abstract

Real-world time-series are rarely purely linear or nonlinear and often contain both these patterns. Therefore, in this article, we have developed four hybrid models by combining linear seasonal autoregressive integrated moving average (SARIMA) and nonlinear support vector regression (NLSVR) models for time-series forecasting. Further, particle swarm optimization (PSO), which is a very efficient population-based global stochastic optimization technique, is employed to estimate the hyperparameters of resultant models. A relevant computer program is written in MATLAB function (m file). The SAS and MATLAB software packages are used for carrying out data analysis. Subsequently, as an illustration, the models are applied to all-India monthly marine products export time-series data. Superiority of hybrid models over individual SARIMA and NLSVR models is demonstrated for the data under consideration using root mean square error (RMSE) and mean absolute error (MAE) criteria.