Hybrid ARIMA-support vector machine model for agricultural production planning

Abstract

In this study we develop the hybrid models for forecasting in agricultural production planning. Real data of Thailand’s orchid export and Thailand’s pork product are used to validate candidate models. Autoregressive Integrate Moving Average (ARIMA) is also selected as a benchmarking to compare other developed models. The main concept of building the models is to combine different forecasting techniques in order to overcome the time-series forecasting errors. The combined models of Support Vector Machine (SVM) and ARIMA are considered as they can be represented both nonlinear and linear values. We perform many experiments on the combination of SVM and ARIMA and select the most precision model, which is the SVM (10) and ARIMA hybrid model, by using statistical criteria. For orchid export case, comparing to ARIMA, the error reduction from MAE, RMSE, and MAPE is 2.46%, 1.96%, and 4.63%, respectively. Moreover, the error reduction from MAE, RMSE, and MAPE is 8.08%, 6.24%, and 6.88%, respectively, for the case of pork product.