Vegetable Price Forecasting Using ARIMA and VAR Modeling

Price forecasting of agricultural products has gained the attention of researchers to help farmers, policymakers, and insurance companies. Price forecasting has been studied by time series analysis over a long decade. Time series models like ARIMA, and VAR provides significant accuracy for forecasting sequence data. Modern researchers rely on machine learning models to predict the future based on past observations. This paper studies the performance of traditional time series models over machine learning models for forecasting vegetable prices for some Indian APMC markets. It has been shown that traditional time series models outperform the widely used machine learning models.