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A comparative study of neural-network & fuzzy time series forecasting techniques - Case study: Marine fish production forecasting

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Title A comparative study of neural-network & fuzzy time series forecasting techniques - Case study: Marine fish production forecasting
 
Creator Yadav, V K
Krishnan, M
Biradar, R S
Kumar, N R
Bharti, V S
 
Subject Fuzzy Time Series
Fuzzy Set
Production
Forecasting
Linguistic Value
Fuzzified production
Fuzzy logical relationships
Back Propagation Algorithm
 
Description 707-716
Various forecasting methods have been developed on the basis of fuzzy
time series data, but accuracy has been matter of concern in these forecasts. Historical
data of marine fish production of India have been taken to implement the model;
as such time series data obtained through sample survey are likely to be
imprecise. Fuzzy sets theory of1 and fuzzy time series models
introduced by2-5, were applied in this study. The forecast to marine fish production have also
been obtained by developing an Artificial Neural Network (ANN) model using Back
propagation algorithm. It is aimed to find the marine fish production forecast
for a lead year by using different fuzzy time series models and back
propagation algorithm for the forecast. Forecasted marine fish production,
obtained through these techniques, has been compared and their performance has
been examined. Present infers that ANN produces more accurate results in
comparison of fuzzy time series methods.
 
Date 2013-12-13T12:45:23Z
2013-12-13T12:45:23Z
2013-10
 
Type Article
 
Identifier 0975-1033 (Online); 0379-5136 (Print)
http://hdl.handle.net/123456789/24811
 
Language en_US
 
Rights CC Attribution-Noncommercial-No Derivative Works 2.5 India
 
Publisher NISCAIR-CSIR, India
 
Source IJMS Vol.42(6) [October 2013]