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