Intuitionistic Fuzzy Set-Based Computational Method for Financial Time Series Forecasting

Bisht, Kamlesh; Kumar, Sanjay

doi:10.1080/16168658.2019.1631557

Cited by 17 publications

(7 citation statements)

References 63 publications

(78 reference statements)

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“…-Taiwan stock exchange corporation (TAIEX): TAIEX is Taiwan stock exchange benchmark index. It is also hugely used in several works [2,7,14,36]. In our work, training data set, collected daily from January to Table 2.…”

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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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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“…Their studies opened a new intensive research field, attracting a significant number of methodological and application-oriented studies. Ones can find in the literature the methodological studies including, e.g., model improvement by simplifying computational method [2,3], applying Fibonacci sequence [5]; high-order FTS [6][7][8][9]; multi-factor [10][11][12]; type-2 fuzzy set [13][14][15]; intuitionistic fuzzy set [16][17][18][19][20][21]; picture fuzzy set [22]; suitably determining 'fuzzy' intervals of the universe of discourse assigned to the pre-specified L-words and their numeric or defuzzification values [8,23,24]; optimizing these 'fuzzy' length [8,25,26]. In the first enrollment forecasting models of Song [2,3] and Chen [5,7], the number of 'fuzzy' intervals of the universe of discourse is 7, and their length is equal.…”

Section: Introductionmentioning

confidence: 99%

Scalable Human Knowledge About Numeric Time Series Variation and Its Role in Improving Forecasting Results

Hieu

Hồ

Phong

et al. 2022

JCC

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Instead of handling fuzzy sets associated with linguistic (L-) labels based on the developers’ intuition immediately, the study follows the hedge algebras (HA-) approach to the time series forecasting problems, in which the linguistic time series forecasting model was, for the first time, proposed and examined in 2020. It can handle the declared forecasting L-variable word-set directly and, hence, the terminology linguistic time-series (LTS) is used instead of the fuzzy time-series (FTS). Instead of utilizing a limited number of fuzzy sets, this study views the L-variable under consideration as to the numeric forecasting variable's human linguistic counterpart. Hence, its word-domain becomes potentially infinite to positively utilize the HA-approach formalism for increasing the LTS forecasting result exactness. Because the forecasting model proposed in this study can directly handle L-words, the LTS, constructed from the numeric time series and its L-relationship groups, considered human knowledges of the given time-series variation helpful for the human-machine interface. The study shows that the proposed formalism can more easily handle the LTS forecasting models and increase their performance compared to the FTS forecasting models when the words’ number grows.

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“…Since IFSs are considered more ideal than fuzzy set in handling uncertainty along with non-determinacy, many researchers (Gangwar and Kumar 2014;Kumar and Gangwar 2016;Wang et al 2016, Abhishekh et al 2018 proposed IFS-based FTS forecasting models. Bisht and Kumar (2018), Egrioglu et al (2019) and Kocak et al (2020) have also explored computational algorithm, functions approach and long short-term memory-based intuitionistic FTS forecasting methods. Recently, have integrated PSO and IFS for fuzzy time series forecasting.…”

Section: Introductionmentioning

confidence: 99%

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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Intuitionistic Fuzzy Set-Based Computational Method for Financial Time Series Forecasting

Cited by 17 publications

References 63 publications

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

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

Scalable Human Knowledge About Numeric Time Series Variation and Its Role in Improving Forecasting Results

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

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