A Novel Method to Optimize Interval Length for Intuitionistic Fuzzy Time Series

Pant, Manish; Shukla, Amit K.; Kumar, Sanjay

doi:10.1007/978-981-16-2712-5_5

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…Distribution based is an algorithm in FTS which is used to determine the length of intervals. Distribution based length can be determined by the following algorithm [10]: 1) Calculate all the absolute differences between 𝐴 𝑖+1 and 𝐴 𝑖 (𝑖 = 1, … , 𝑛 − 1) as the first differences and the average of the first differences. 2) According to the average, determine the base for length of intervals based by following Table 1.…”

Section: Distribution Basedmentioning

confidence: 99%

Distribution Based Fuzzy Time Series Markov Chain Models for forecasting Inflation in Bandung

Pratiwi

Rachmatin

Marwati

2022

KUBIK

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This study discusses the application of the Fuzzy Time Series Markov Chain method which was developed by determining the length of the interval using the distribution method. In the fuzzy forecasting method, the determination of the length of the interval is an important thing that will affect the accuracy of the forecasting results. The development of this forecasting model aims to get better forecasting accuracy results. In this study, general inflation data for the city of Bandung is used for the period January 2016 – June 2021. The data is divided into two groups, namely in sample data and out sample data with a ratio of 90: 10. In the data processing process, the Python programming language is used. Based on the accuracy test using the MAPE method, it can be concluded that this method provides better forecasting results with a MAPE value of 1.16%.

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Section: Distribution Basedmentioning

confidence: 99%

Distribution Based Fuzzy Time Series Markov Chain Models for forecasting Inflation in Bandung

Pratiwi

Rachmatin

Marwati

2022

KUBIK

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Fuzzy time series forecasting based on hesitant fuzzy sets, particle swarm optimization and support vector machine-based hybrid method

Pant

Kumar

2021

Granul. Comput.

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In this paper, we propose hesitant fuzzy sets-based hybrid time series forecasting method using particle swarm optimization and support vector machine. Length of unequal intervals, weights of intervals and process of defuzzification are major factors that affect the forecasting accuracy of hesitant fuzzy sets-based time series models. The proposed hybrid fuzzy time series forecasting method uses hesitant fuzzy sets which are constructed using fuzzy sets with equal and unequal length intervals. Particle swarm optimization and linear programming are used to optimize length of unequal intervals and weights of equal and unequal intervals. The proposed hybrid method of time series forecasting uses support vector machine for setting input-target pattern for defuzzification. Outperformance of proposed hybrid method of time series forecasting method is revealed by applying it on widely used time series data of enrollments of the University of Alabama, market share price of State Bank of India share at Bombay stock exchange and car sell in Quebec City of Canada. Validity of the proposed hybrid fuzzy time series forecasting method is verified using values of Willmott index and tracking signal.

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A new intuitionistic fuzzy time series method based on the bagging of decision trees and principal component analysis

Yücesoy,

Egrioglu,

Bas

2023

Granul. Comput.

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Intuitionistic fuzzy time series methods provide a good alternative to the forecasting problem. It is possible to use the historical values of the time series as well as the membership and non-membership values obtained for the historical values as effective factors in improving the forecasting performance. In this study, a high order single variable intuitionistic fuzzy time series reduced forecasting model is first introduced. A new forecasting method is proposed for the solution of the forecasting problem in which the functional structure between the historical information of the intuitionistic time series and the forecast is obtained by bagging of decision trees based on the high order single variable intuitionistic fuzzy time series reduced forecasting model.In the proposed method, the intuitionistic fuzzy c-means clustering method is used to create intuitionistic fuzzy time series. To create a simpler functional structure with Bagging of decision trees, the input data from lagged variables, memberships, and non-membership values are subjected to dimension reduction by principal component analysis. The performance of the proposed method is compared with popular forecasting methods in the literature for ten different time series randomly obtained from the S&P500 stock market. According to the results of the analyses, the forecasting performance of the proposed method is better than both classical forecasting methods and some popular shallow and deep neural networks. KeywordsForecasting . Intuitionistic fuzzy time series . Bagging of decision tree . Principal component analysis . Intuitionistic fuzzy c-means clustering

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A Novel Method to Optimize Interval Length for Intuitionistic Fuzzy Time Series

Cited by 5 publications

References 24 publications

Distribution Based Fuzzy Time Series Markov Chain Models for forecasting Inflation in Bandung

Distribution Based Fuzzy Time Series Markov Chain Models for forecasting Inflation in Bandung

Fuzzy time series forecasting based on hesitant fuzzy sets, particle swarm optimization and support vector machine-based hybrid method

A new intuitionistic fuzzy time series method based on the bagging of decision trees and principal component analysis

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