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2019 | OriginalPaper | Chapter

Fuzzy Time Series Model for Unequal Interval Length Using Genetic Algorithm

Authors : Shanoli Samui Pal, Samarjit Kar

Published in: Information Technology and Applied Mathematics

Publisher: Springer Singapore

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Abstract

Time series analysis and forecasting depend on the observed historical data. As it deals with numerous number of information, imprecision occurs due to many reasons like truncation error, mechanical fault, noise. In such cases, fuzzy concept works well to handle impreciseness. Fuzzy time series forecasting methodology consists of defining universe of discourse (UOD), fuzzification of time series data points, assigning relationships between consecutive data points and defuzzification to get back the forecasting results in real domain. In this paper, UOD of a time series has been defined, unequal partitions of the UOD are established and then relationship among consecutive data points is evaluated to get the forecasting models. Genetic algorithm has been used in partitioning the UOD unequally and establishing the relationship. Forecasting model is applied on enrollment of University of Alabama, BSE sensex time series, and Shenzhen stock exchange data. The results are compared with conventional fuzzy time series models.

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previous chapter An Integrated Imperfect Production–Inventory Model with Optimal Vendor Investment and Backorder Price Discount

next chapter A Multi-item EPQ Model with Variable Demand in an Imperfect Production Process

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Title: Fuzzy Time Series Model for Unequal Interval Length Using Genetic Algorithm
Authors: Shanoli Samui Pal
Samarjit Kar
Publisher: Springer Singapore
Book: Information Technology and Applied Mathematics
Print ISBN: 978-981-10-7589-6

Electronic ISBN: 978-981-10-7590-2

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-981-10-7590-2_15

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