Intuitionistic fuzzy time series forecasting method for non-stationary time series data with suitable number of clusters and different window size for fuzzy rule generation

Dixit, Ankit; Jain, Shikha

doi:10.1016/j.ins.2022.12.015

Cited by 16 publications

(2 citation statements)

References 43 publications

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“…Their achievement relies on the precision of the dataset utilized to train the algorithm, together with the proficiency of the modeler in formulating the system's logic. For a fuzzy system to generate dependable predictions, it must undergo training using a data set that is both comprehensive and precise [14]. Interpreting fuzzy systems can pose difficulties.…”

Section: Methodsmentioning

confidence: 99%

A Performance Comparison of Neural Networks and Fuzzy Systems for Time Series Forecasting

Woong

2024

JMC

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Artificial neural networks and fuzzy structures have gained significant popularity in the last decade for time series forecasting. The objective is to conduct a performance comparison of various strategies to determine which ones are more effective for time series forecasting. The dataset provides instruction and evaluates forecasting models, utilizing artificial neural networks and fuzzy architectures. The observation evaluates the overall effectiveness of the forecasting models and the use of the root mean square error and means absolute error measures. This comparison analysis provides initial insights into the efficacy of artificial neural networks and fuzzy structures for predicting time series data. In predicting time series data, this study examines the precision of two renowned artificial intelligence systems, Neural Networks and Fuzzy structures. To evaluate the two algorithms, two distinct types of time series were utilized: a synthetic dataset consisting of 150 variables and a real-world dataset including 129 data points about monetary forecasts. The models' forecasting accuracy, training duration, and generalization abilities were compared. The findings validated that neural network surpassed fuzzy structures in all performance metrics when handling synthetic data. This research emphasizes the capabilities of artificial neural networks and fuzzy structures in addressing complicated forecasting problems. It demonstrates that both techniques may be utilized for predicting future time series values.

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Section: Methodsmentioning

confidence: 99%

A Performance Comparison of Neural Networks and Fuzzy Systems for Time Series Forecasting

Woong

2024

JMC

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“…A forecasting method is proposed for solving the high-order intuitionistic fuzzy time series forecasting model by Gautam and Singh (2018). Fan et al (2020), Chen et al (2021), Pant and Kumar (2022), Wang et al (2023), and Dixit and Jain (2023) are other recent studies of forecasting methods using intuitionistic fuzzy sets. One of the most well-known fuzzy inference systems is the fuzzy regression functions approach, which in recent years has been able to produce successful results in solving the forecasting problem.…”

Section: Introductionmentioning

confidence: 99%

Robust Picture Fuzzy Regression Functions Approach Based on M-Estimators for the Forecasting Problem

Bas,

Egrioglu

2024

Comput Econ

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A picture fuzzy regression function approach is a fuzzy inference system method that uses as input the lagged variables of a time series and the positive, negative and neutral membership values obtained by picture fuzzy clustering method. In a picture fuzzy regression functions method, the parameter estimation is also obtained by ordinary least squares method. Since the picture fuzzy regression functions approach is based on the ordinary least squares method, the forecasting performance decreases when there are outliers in the time series. In this study, a picture fuzzy regression function approach that can be used even in the presence of outliers in a time series is proposed. In the proposed method, the parameter estimation for the picture fuzzy regression function approach is performed based on robust regression with Bisquare, Cauchy, Fair, Huber, Logistic, Talwar and Welsch functions. The forecasting performance of the proposed method is evaluated on the time series of the Spanish and the London stock exchange time series. The forecasting performance of these time series are evaluated separately for both the original and outlier cases. Besides, the proposed method is compared with several different fuzzy regression function approaches and a neural network method. Based on the results of the analysis, it is concluded that the proposed method outperforms the other methods even when the time series contains both original and outliers.

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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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Intuitionistic fuzzy time series forecasting method for non-stationary time series data with suitable number of clusters and different window size for fuzzy rule generation

Cited by 16 publications

References 43 publications

A Performance Comparison of Neural Networks and Fuzzy Systems for Time Series Forecasting

A Performance Comparison of Neural Networks and Fuzzy Systems for Time Series Forecasting

Robust Picture Fuzzy Regression Functions Approach Based on M-Estimators for the Forecasting Problem

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

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