Hesitant fuzzy set based computational method for financial time series forecasting

Bisht, Kamlesh; Kumar, Sanjay

doi:10.1007/s41066-018-00144-4

Cited by 30 publications

(11 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since HFS provides a powerful tool to remove the conciliation all the grade of membership of time series datum during fuzzification using more than one fuzzy set, Bisht and Kumar (2016) introduced its application in FTS time series forecasting. Bisht and Kumar (2019) also developed a computational algorithm for FTS forecasting method using HFS. Recently, Iqbal and Zhang (2020) integrated clustering and modified smoothing approach and proposed HFS-based FTS model.…”

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.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hesitant fuzzy sets and probabilistic fuzzy sets represent other extensions of fuzzy sets used for fuzzy time series forecasting [4,24,25]. To avoid the overfitting problem of single prediction models, several studies introduced combinations of fuzzy neural networks utilizing both interval type-2 fuzzy sets [26] and intuitionistic fuzzy sets [27].…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy Neural Network for Time Series Forecasting - The Case of Metal Prices

Hájek

Olej

Froelich

et al. 2021

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Forecasting time series is an important problem addressed for years. Despite that, it still raises an active interest of researchers. The main issue related to that problem is the inherent uncertainty in data which is hard to be represented in the form of a forecasting model. To solve that issue, a fuzzy model of time series was proposed. Recent developments of that model extend the level of uncertainty involved in data using intuitionistic fuzzy sets. It is, however, worth noting that additional fuzziness exhibits nonlinear behavior. To cope with that issue, we propose a time series model that represents both high uncertainty and non-linearity involved in the data. Specifically, we propose a forecasting model integrating intuitionistic fuzzy sets with neural networks for predicting metal prices. We validate our approach using five financial multivariate time series. The results are compared with those produced by state-of-the-art fuzzy time series models. Thus, we provide solid evidence of high effectiveness of our approach for both one-and five-dayahead forecasting horizons.

show abstract

“…Wang (2018) used a fuzzy time series forecasting method for big data analysis. Bisht and Kumar (2019) used hesitant fuzzy sets based on the computational method for financial time series forecasting. Egrioglu et al (2019) proposed a forecasting method for single-variable high-order intuitionistic fuzzy time series forecasting model.…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic fuzzy time series functions approach for time series forecasting

2020

View full text Add to dashboard Cite

Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods.

show abstract

Hesitant fuzzy set based computational method for financial time series forecasting

Cited by 30 publications

References 57 publications

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

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

Intuitionistic Fuzzy Neural Network for Time Series Forecasting - The Case of Metal Prices

Intuitionistic fuzzy time series functions approach for time series forecasting

Contact Info

Product

Resources

About