Agricultural product forecasting using machine learning approach

Abstract

In this study we develop the machine learning models for forecasting agricultural products. The main concept of building the models is because machine learning is flexible and convenient to implement and it can be potential applications for a naïve user. The proposed model of Support Vector Machine (SVM) is able to forecast nonlinear or linear forecasting function upon kernel function. Many experiments were performed on the development of SVM and the most precision model by using statistical criteria was also selected. Real data of Thailand’s Pacific white shrimp export and Thailand’s produced chicken were used to validate candidate models. Autoregressive Integrate Moving Average (ARIMA) is also selected as a benchmarking to compare other developed models. For Pacific white shrimp export case, comparing to ARIMA, the error reduction from MAE, RMSE, and MAPE is 25.76%, 18.11%, and 19.05%, respectively. Moreover, the error reduction from MAE, RMSE, and MAPE is 21.78%, 18.76%, and 18.11%, respectively, for the case of produced chicken.