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Title PAKISTAN GRAM PRODUCTION FORECASTING USING BAYESIAN TIME SERIES MODELING

Author(s) ADIL FAIZAN

Abstract The Bayesian approach/statistics, is a statistical decision approach that provides a tool for combining prior probabilities and their distribution about the nature of states. It provides tool to the people to modernize their views in the indication of fresh improved record or data. When working with such issue along time series models is that they too fit commonly when estimating models have large numbers of attributes above somewhat short length/time periods. In our case, this is not such a problem but possibly be when eyeing many attributes, these are common quite in economic prediction. One explanation to over fit problem is using a Bayesian approach, which opens a way to enforce specific priors on attributes. The aim of this thesis is to forecast the production of Gram, which include different attributes like gram cultivation area, production of Gram, the yield of a gram, the cost and prices of gram. For time collection or series data, ARIMA based state space modeling is used to forecast different future attributes of rabbi food crops of Pakistan including gram.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Engineering

Language English

Publication Date 2022-09-20

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