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A Study of Fuzzy Time-series Models in Agriculture

KrishiKosh

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Field Value
 
Title A Study of Fuzzy Time-series Models in Agriculture
M Sc
 
Creator SUMIT CHOWDHURY
 
Contributor Himadri Ghosh)
 
Subject forecasting, sets, productivity, sampling, manpower, statistical methods, markets, byproducts, area, paper
 
Description T-8627
The time-series modelling and forecasting investigate relations between present and past observations for the sequential set of measurement over time. The area has been widely studied and traditional forecasting are frequently conducted by various statistical tools. However, the classical time-series theory assumes values of the response variable to be „crisp‟ or „precise‟, which is quite often violated in reality, and cannot handle „fuzziness‟ in the underlying system, a problem which can be solved by fuzzy time-series analysis. Works had been done on various models for fuzzy time-series analysis. However, not much of a work is done till date on forecasting of out-of-sample data using fuzzy time-series models. In this paper, attempts have been made to develop a new methodology for fuzzy time-series modelling using non-convex membership functions and perform prediction of out-of-sample data. The performance of the methodology has been critically assessed by comparing it with the existing methodologies at various stages viz. modelling, validation and out-of-sample forecast.
 
Date 2016-09-20T19:22:59Z
2016-09-20T19:22:59Z
2012
 
Type Thesis
 
Identifier http://krishikosh.egranth.ac.in/handle/1/77833
 
Format application/pdf
 
Publisher IARI, INDIAN AGRICULTURAL STATISTICS RESEARCH INSTITUTE