District Wise Price Forecasting of Wheat in Pakistan using Deep Learning

• URL: http://arxiv.org/abs/2103.04781v1
• Date: Fri, 5 Mar 2021 06:13:51 GMT
• Title: District Wise Price Forecasting of Wheat in Pakistan using Deep Learning
• Authors: Ahmed Rasheed, Muhammad Shahzad Younis, Farooq Ahmad, Junaid Qadir, and Muhammad Kashif
• Abstract summary: Wheat is the main agricultural crop of Pakistan and is a staple food requirement of almost every Pakistani household. 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. The proposed methodology presented significantly improved results versus other conventional machine learning and statistical time series analysis methods.
• Score: 3.5281018826570536
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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