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Erschienen in:
Hesitant-Intuitionistic Trapezoidal Fuzzy Prioritized Operators Based on Einstein Operations with Their Application to Multi-criteria Group Decision-Making Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Handling Seasonal Pattern and Prediction Using Fuzzy Time Series Model

verfasst von : Mahua Bose, Kalyani Mali

Erschienen in: Algorithms in Machine Learning Paradigms

Verlag: Springer Singapore

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Abstract

Seasonal variation is one of the important components of the time series. There are many techniques available in the literature to deal with the problem of seasonality. A few hybrid fuzzy time series models investigated the problem of forecasting in the presence of seasonal variation. But these techniques follow complex computational procedures. The aim of this present study is to develop a new fuzzy time series forecasting model that can process seasonal patterns present in the data directly without any seasonal adjustment by applying certain mathematical techniques. The proposed Neuro-uzzy model is capable of extracting the seasonal pattern from the training set and forecasting the future pattern. This model makes use of Self-organizing map (SOM) for clustering similar patterns. Performance of the model is evaluated using Rainfall data and Milk Production data.

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Vorheriges Kapitel Fact-Based Expert System for Supplier Selection with ERP Data
Nächstes Kapitel Automatic Classification of Fruits and Vegetables: A Texture-Based Approach
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Metadaten
Titel
Handling Seasonal Pattern and Prediction Using Fuzzy Time Series Model
verfasst von
Mahua Bose
Kalyani Mali
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Algorithms in Machine Learning Paradigms

Print ISBN: 978-981-15-1040-3

Electronic ISBN: 978-981-15-1041-0

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-981-15-1041-0_4

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