AN ANALYSIS OF PRICE FORECASTING TECHNIQUES FOR ONION AND TOMATO CROPS
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Date
2015-07-14
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UNIVERSITY OF AGRICULTURAL SCIENCES GKVK, BENGALURU
Abstract
Agricultural price forecasts are an integral component of trade and policy
analysis. As the prices of agricultural commodities directly influence the real income
of consumers and it also affects the consumers’ access to food. For the purpose of this
study two such agricultural commodities are selected i.e. onion and tomato because of
huge demand and price fluctuation. The present study was conducted using ten years
monthly prices and arrivals of onion and tomato at Bengaluru and Kolar APMC
market, respectively. The data for the above period was collected from Kirshi Marata
Vahini website and the selection of markets for the study was done on the basis of
maximum quantity of arrivals for the markets. The forecasting models used for onion
crop are Auto Regressive Integrated Moving Average (ARIMA), Seasonal Auto
Regressive Integrated Moving Average (SARIMA) and Generalized Auto Regressive
Conditional Heteroscedastic (GARCH). The forecasting models used for tomato crop
are ARIMA, Auto Regressive Integrated Moving Average exogenous variable
(ARIMAX) and GARCH. These models were used to forecast prices for next 12
months. In case of onion price, SARIMA model was found to be the best forecasting
model by giving a Minimum Absolute percentage error (MAPE) value 21.24 per cent.
In tomato crop, ARIMAX model was found to be best forecasting model with a
minimum MAPE value of 59.06 per cent. The identification of the best forecasting
model would help the producers, consumers as well as suppliers in taking appropriate
decisions.
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