Regression-Based AGRO Forecasting Model

Prediction plays an important role everywhere particularly in business, technology, and many others. It helps all types of organizations to improve profits and reduce the loss by taking timely decisions. Agriculture is also like an organization where farmers suffer with the loss most of the time in this business. This could be mainly because, there is no system to ensure the synchronization between the demand and supply for various food commodities required by the society. Science, enormous amount of data from different authorized sources like government websites revealing the demand and supply of various food commodities for forgoing period, this paper proposes a novel regression based AGRO forecasting model. This could help the farmers to make timely decisions and work towards fulfilling the actual needs of the society and avoiding putting themselves into the loss by growing unnecessary crops. Proposed model has been implemented using MapReduce parallel programming approach with Hadoop Distributed File System. This processes time series data with Regression model for predicting the demand, supply and price for the agricultural commodities in distributed environment. Resulting forecasted values are in the range of real values.