Title: Price forecasting system for crops at the sowing time

Authors: Sanjeev Kumar; Shivashish Upadhyay; Sonal Singh; Saumya Sharma

Addresses: Department of Computer Science and Engineering, ABESIT Ghaziabad, India ' Department of Computer Science and Engineering, ABESIT Ghaziabad, India ' Department of Computer Science and Engineering, ABESIT Ghaziabad, India ' Department of Computer Science and Engineering, ABESIT Ghaziabad, India

Abstract: There is a high volatile of price in the field of agriculture. We are trying to develop a price-forecasting model to predict the price of crops at the time of sowing. Prediction of price prior sowing the crop or before selling the crop is a key aspect of effective farm management. By using the past data and analysing its trend, we can predict the future trend and future price of the given crops. The past data regarding price of crops is tested for prediction purpose. Our developed model predicts the price of the crops before sowing seeds. Prediction of the price of crop is done by using auto regressive integrated moving average (ARIMA) model. We have used the rolling mean, standard deviation, Dickey-Fuller test, partial autocorrelation, function and auto-correlation function to verify the stationarity of the data. Further we have implemented auto regressive (AR) model, moving average (MA) model an auto regressive integrated moving average (ARIMA) model for the given time series data. Potato crop is used for forecasting purposes and the data is taken from a reliable government site. Our objective is to develop a price forecasting system that is helpful to maximise the profits of farmers.

Keywords: time series; autocorrelation; mean; variance; forecasting; predictive models; rolling mean test; Dickey-Fuller test; Durbin-Watson statistic; auto regression integrated moving average; ARIMA.

DOI: 10.1504/IJAITG.2021.121284

International Journal of Agriculture Innovation, Technology and Globalisation, 2021 Vol.2 No.3, pp.204 - 221

Received: 27 Mar 2020
Accepted: 30 Oct 2020
Published online: 03 Mar 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article