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Wheat is the main agricultural crop of Pakistan and is a staple foodrequirement of almost every Pakistani household making it the main strategiccommodity of the country whose availability and affordability is thegovernment's main priority. Wheat food availability can be vastly affected bymultiple factors included but not limited to the production, consumption,financial crisis, inflation, or volatile market. The government ensures foodsecurity by particular policy and monitory arrangements, which keeps uppurchase parity for the poor. Such arrangements can be made more effective if adynamic analysis is carried out to estimate the future yield based on certaincurrent factors. Future planning of commodity pricing is achievable byforecasting their future price anticipated by the current circumstances. Thispaper presents a wheat price forecasting methodology, which uses the price,weather, production, and consumption trends for wheat prices taken over thepast few years and analyzes them with the help of advance neural networksarchitecture Long Short Term Memory (LSTM) networks. The proposed methodologypresented significantly improved results versus other conventional machinelearning and statistical time series analysis methods.

District Wise Price Forecasting of Wheat in Pakistan using Deep Learning