Intuitionistic Fuzzy Sets Based Method for Fuzzy Time Series Forecasting

Joshi, Bhagawati Prasad; Kumar, Sanjay

doi:10.1080/01969722.2012.637014

Cited by 96 publications

(44 citation statements)

References 20 publications

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“…To cope with such situations, Zadeh introduced fuzzy sets and which was further extended by Atanassov by introducing the concept of intuitionistic fuzzy set (IFS), which is characterized in such a way that the sum of the support for membership and support against membership is less than or equal to one. Due to this characteristic, IFS theory is one of the successful and powerful tools to deal with imprecise, vague and ambiguous information, and receives attention to many practitioners to deal with real life situations. But, the aggregation of all the performances in dealing with real life problems is a very critical step to obtain decisions.…”

Section: Introductionmentioning

confidence: 99%

Confidence levelsq‐rung orthopair fuzzy aggregation operators and its applications to MCDM problems

Joshi¹,

Gegov

2019

Int J Intell Syst

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The concept of q-rung orthopair fuzzy set (q-ROFS) is the extension of intuitionistic fuzzy set (IFS) in which the sum of the qth power of the support for and the qth power of the support against is bounded by one. Therefore, the q-ROFSs are an important way to express uncertain information in broader space, and they are superior to the IFSs and the Pythagorean fuzzy sets. In this paper, the familiarity degree of the experts with the evaluated objects is incorporated to the initial assessments under q-rung orthopair fuzzy environment. For this, some aggregation operators are proposed to combine these two types of information. Their some important properties are also well proved. Furthermore, these developed operators are utilized in a multicriteria decision-making approach and demonstrated with a real life problem of customers' choice. Then, the experimental results are compared with other existing methods to show its superiority over recent research works.

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Section: Introductionmentioning

confidence: 99%

Confidence levelsq‐rung orthopair fuzzy aggregation operators and its applications to MCDM problems

Joshi¹,

Gegov

2019

Int J Intell Syst

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“…-Bombay stock exchange (BSE): BSE's popular equity index -the S&P BSE SENSEX-is India's mostly tracked stock market benchmark index. There are several work i.e., stock market analysis, forecasting models on BSE data [11,17]. In this paper, training data set, collected daily from January to December, 2014 (241 data points) and testing data set, collected daily from January to April, 2015 (84 data points) are used [6].…”

Section: Data Descriptionmentioning

confidence: 99%

Forecasting stock market price by using fuzzified Choquet integral based fuzzy measures with genetic algorithm for parameter optimization

Pal

Kar

2020

RAIRO-Oper. Res.

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In this paper, fuzzified Choquet integral and fuzzy-valued integrand with respect to separate measures like fuzzy measure, signed fuzzy measure and intuitionistic fuzzy measure are used to develop regression model for forecasting. Fuzzified Choquet integral is used to build a regression model for forecasting time series with multiple attributes as predictor attributes. Linear regression based forecasting models are suffering from low accuracy and unable to approximate the non-linearity in time series. Whereas Choquet integral can be used as a general non-linear regression model with respect to non classical measures. In the Choquet integral based regression model parameters are optimized by using a real coded genetic algorithm (GA). In these forecasting models, fuzzified integrands denote the participation of an individual attribute or a group of attributes to predict the current situation. Here, more generalized Choquet integral, i.e., fuzzified Choquet integral is used in case of non-linear time series forecasting models. Three different real stock exchange data are used to predict the time series forecasting model. It is observed that the accuracy of prediction models highly depends on the non-linearity of the time series.

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“…Cheng et al [21] proposed a fuzzy time series forecasting model with a two-stage linguistic partition method. Joshi and Kumar [22] proposed a computational model of forecasting for fuzzy time series based on intuitionistic fuzzy sets, in which the degree of nondeterminacy is used to establish fuzzy logical relations.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Fuzzy Relational Modeling for Fuzzy Time Series Forecasting

Kuo

Chen

2015

Int. J. Fuzzy Syst.

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The use of fuzzy time series has attracted considerable attention in studies that aim to make forecasts using uncertain information. However, most of the related studies do not use a learning mechanism to extract valuable information from historical data. In this study, we propose an evolutionary fuzzy forecasting model, in which a learning technique for a fuzzy relation matrix is designed to fit the historical data. Taking into consideration the causal relationships among the linguistic terms that are missing in many existing fuzzy time series forecasting models, this method can naturally smooth the defuzzification process, thus obtaining better results than many other fuzzy time series forecasting models, which tend to produce stepwise outcomes. The experimental results with two real datasets and four indicators show that the proposed model achieves a significant improvement in forecasting accuracy compared to earlier models.

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Intuitionistic Fuzzy Sets Based Method for Fuzzy Time Series Forecasting

Cited by 96 publications

References 20 publications

Confidence levelsq‐rung orthopair fuzzy aggregation operators and its applications to MCDM problems

Confidence levelsq‐rung orthopair fuzzy aggregation operators and its applications to MCDM problems

Forecasting stock market price by using fuzzified Choquet integral based fuzzy measures with genetic algorithm for parameter optimization

Evolutionary Fuzzy Relational Modeling for Fuzzy Time Series Forecasting

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