International Journal of Computer
Trends and Technology

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Volume 4 | Issue 7 | Year 2013 | Article Id. IJCTT-V4I7P160 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I7P160

Long Term Forecasting with Fuzzy Time Series and Neural Network: a comparative study using Sugar production data

Ankur Kaushik , A.K.Singh

Citation :

Ankur Kaushik , A.K.Singh, "Long Term Forecasting with Fuzzy Time Series and Neural Network: a comparative study using Sugar production data," International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 7, pp. 2299-2305, 2013. Crossref, https://doi.org/10.14445/22312803/IJCTT-V4I7P160

Abstract

Forecasting of time series that have seasonal and other variations remains an important problem for forecasters. This paper presents a neural network (NN) approach along with a fuzzy time series methods to forecasting sugar production in India. The agriculture production and productivity is one of the such processes, which is not governed by any deterministic process due to highly non linearity caused by various effective production parameters like weather, rainfall, diseases, disaster ,area of cultivation etc. The study uses the fuzzy set theory and applies different fuzzy time series models to forecast the production of sugar in India. The historical data of sugar production from Food Corporation of India have been taken to investigate the results. The sugar production forecast, obtained through these models has been compared and their performance has been examined.

Keywords

Fuzzy Time Series, Fuzzy Set, Production, Forecasting, Linguistic Value, high order model

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