Journal Article

Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India

Paul, A. K., Paul, Ranjit Kumar, Yeasin, Md, Kumar, Pramod, Balasubramanian, M., Roy, H. S., Gupta, Ajit, Kumar, Prabhakar

2022

Overview

Price forecasting of perishable crop like vegetables has importance implications to the farmers, traders as well as consumers. Timely and accurate forecast of the price helps the farmers switch between the alternative nearby markets to sale their produce and getting good prices. The farmers can use the information to make choices around the timing of marketing. For forecasting price of agricultural commodities, several statistical models have been applied in past but those models have their own limitations in terms of assumptions. In recent times, Machine Learning (ML) techniques have been much successful in modeling time series data. Though, numerous empirical studies have shown that ML approaches outperform time series models in forecasting time series, but their application in forecasting vegetables prices in India is scared. In the present investigation, an attempt has been made to explore efficient ML algorithms e.g. Generalized Neural Network (GRNN), Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machine (GBM) for forecasting wholesale price of Brinjal in seventeen major markets of Odisha, India. An empirical comparison of the predictive accuracies of different models with that of the usual stochastic model i.e. Autoregressive integrated moving average (ARIMA) model is carried out and it is observed that ML techniques particularly GRNN performs better in most of the cases. The superiority of the models is established by means of Model Confidence Set (MCS), and other accuracy measures such as Mean Error (ME), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Prediction Error (MAPE). To this end, Diebold-Mariano test is performed to test for the significant differences in predictive accuracy of different models. Among the machine learning techniques, GRNN performs better in all the seventeen markets as compared to other techniques. RF performs at par with GRNN in four markets. The accuracies of other techniques such as SVR, GBM and ARIMA are not up to the mark.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Agricultural commodities / Agriculture / Algorithms / Analysis / Autoregressive models / Biology and Life Sciences / Computer and Information Sciences / Decision trees / Economic forecasting / Error analysis / Farm produce / Farmers / Forecasting / Forecasts and trends / Horticulture / Learning algorithms / Machine learning / Mathematical models / Model accuracy / Modelling / Neural networks / People and Places / Physical Sciences / Prices / Prices and rates / Pricing / Research and Analysis Methods / Root-mean-square errors / Social Sciences / Statistical analysis / Statistical methods / Statistical models / Stochastic models / Stochasticity / Support vector machines / Time series / Vegetables