Predicting prices of agricultural commodities in Thailand using combined approach emphasizing on data pre-processing technique

Abstract

In this research, a combined approach emphasizing on data pre-processing technique is developed to forecast prices of agricultural commodities in Thailand. The future prices play significant role in decision making to cultivate crops in next year. The proposed model takes ability of MODWT to decompose original time series data into more stable and explicit subseries, and SVR model to formulate complex function of forecasting. The experimental results indicated that the proposed model outperforms traditional forecasting models based on MAE and MAPE criteria. Furthermore, the proposed model reveals that it is able to be a useful forecasting tool for prices of agricultural commodities in Thailand

Keywords

• combined approach
• agricultural commodity price
• discrete wavelet transform
• ARIMA
• support vector regression