Time Series Forecasting of Price of Agricultural Products Using Hybrid Methods

Abstract

Accurate prediction of crop prices assists farmers to decide the best time to sell their produce so as to get maximum benefit and assists Government for post-harvest storage and management of the produce so as to stabilize the price volatility throughout the year. At the same time, pricing of crop depends on various factors including the amount of cultivation, demand of consumers, climate, etc. Hence, the prediction of crop prices is a challenging and important problem. Inspired from this, in this study, we have proposed two additive hybrid methods (Additive-ETS-SVM, Additive-ETS-LSTM) and five multiplicative hybrid methods (Multiplicative-ETS-ANN, Multiplicative-ETS-SVM, Multiplicative-ETS-LSTM, Multiplicative-ARIMA-SVM, Multiplicative-ARIMA-LSTM) to predict the monthly retail and wholesale price of three most commonly used vegetable crops of India, namely, tomato, onion, and potato (TOP). The obtained results are compared with two most promising statistical models, three leading machine learning models and five hybrid methods existing in the literature. Extensive statistical analyses of simulation results considering mean absolute error (MAE), symmetric mean absolute percentage error (SMAPE), and root mean square error (RMSE) confirm the superiority of the hybrid methods in predicting the TOP prices.