Forecasting of Onion Prices in Bangalore Market: An Application of Time Series Models

M. Areef, S. Rajeswari, N. Vani and G. Mohan Naidu

Indian Journal of Agricultural Economics, 2020, vol. Volume 75, issue Number 2

Abstract: The present study has made an attempt to identify the best forecasting model to predict the onion prices from January, 2018 to June, 2018. The employed time series models were ARIMA (Box-Jenkins model); Artificial Neural Network (ANN); and Exponential Smoothing Models (Single, Double and Winters) to predict future prices of Bangalore onion market. For ARIMA technique, (1,1,0) (1,1,1) model was selected, the adequacy of the model was judged by maximum R-Square (72.91), minimum MAPE (21.16), RMSE (379.18) and MAE (228.18). In the case of ANN technique, ten best performing neural networks were analysed. Among them ANN2 was found as a good fitted model with high R2 value equal to 0.90 and lowest average absolute error value of 205.63 per cent with highest value of correlation coefficient 0.95. The forecasted values of onion prices are ranging from 1380 to 847 per quintal for the months from January to June 2018. Among the different exponential smoothing techniques, the simple exponential smoothing (SES) model was the preferred model for forecasting onion price due to the minimum value of MAPE (21), MAD (231) and MSD (144255). Comparing the actual prices with the predicted prices the results revealed that among different models, ANN model was the most suitable technique to predict future prices accurately for onion in Bangalore market. The identification of the best forecasting model and accurate forecasting of market prices would help the farmers, consumers, wholesalers as well as government in order to take appropriate decisions.

Keywords: Demand; and; Price; Analysis (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/345131/files/F ... ngalore%20Market.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:inijae:345131

DOI: 10.22004/ag.econ.345131

Access Statistics for this article

More articles in Indian Journal of Agricultural Economics from Indian Society of Agricultural Economics Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().