An approach to handel nondeterminism in fuzzy time series forecasting by hesitant fuzzy sets

Bisht, Kamlesh; Dhyani, Makrand; Kumar, Sanjay

doi:10.1109/icaccaf.2017.8344733

Cited by 2 publications

(1 citation statement)

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“…To our knowledge, the earliest fuzzy time series based on an improved version of the fuzzy set theory was proposed by Joshi & Kumar in 2012 [2], [3], where they proposed a fuzzy time series forecasting method based on intuitionistic fuzzy set. Since then, there have been quite a development in fuzzy time series based on improved versions of fuzzy set theory, such as Gangwar & Kumar in 2014 [4], where they proposed a fuzzy time series forecasting method based on intuitionistic fuzzy set utilizing CPDA (cumulative probability distribution approach) partition method; Kumar & Gangwar in 2015 [40], where they proposed a fuzzy time series forecasting method induced by intuitionistic fuzzy sets; Bisht & Kumar in 2016 [41], where they proposed a fuzzy time series forecasting method based on hesitant fuzzy set utilizing triangular and CPDA partition method; Joshi et al in 2016 [42], where they introduced intuitionistic fuzzy time series forecasting method; and improved by Kumar & Gangwar in the same year [43]; Bisht, et al in 2017 [44], where they proposed a fuzzy time series forecasting method based on hesitant fuzzy set utilizing triangular and Gaussian partition method; Bisht, et al in 2018 [45], where they proposed a fuzzy time series forecasting method integrating intuitionistic fuzzy set and dual hesitant fuzzy set; Gupta & Kumar in 2018 [46], where they proposed a fuzzy time series forecasting method based on hesitant probabilistic fuzzy set; and Gupta & Kumar in 2019 [47], where they proposed a fuzzy time series forecasting method based on probabilistic fuzzy set. In 2019, Abdel-Basset, et al [48] introduced a fuzzy time series forecasting method based on single-valued neutrosophic set, or known as neutrosophic time series (NTS).…”

Section: Introductionmentioning

confidence: 99%

A Novel Single Valued Neutrosophic Hesitant Fuzzy Time Series Model: Applications in Indonesian and Argentinian Stock Index Forecasting

et al. 2020

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This paper proposed a novel first-order single-valued neutrosophic hesitant fuzzy time series (SVNHFTS) forecasting model. Our aim is to improve the previously proposed neutrosophic time series (NTS) model by incorporating the degree of the hesitancy using single-valued neutrosophic hesitant fuzzy set (SVNHFS) model instead of single-valued neutrosophic set (SVNS). Our paper's novelty is that we incorporate an algorithm that automatically converts the crisp dataset into the neutrosophic set that eliminates the need for experts' input or opinions in determining the membership in each of the partitioned neutrosophic set. We also incorporate Markov Chain algorithm in the de-neutrosophication process to include the weightage of the repeating neutrosophic logical relationships (NLRs). Our paper's significant contribution is to add to the existing body of knowledge related to fuzzy time series (FTS) by developing a new FTS model based on SVNHFS, one of the improved version of fuzzy sets, since this area of research is still relatively underdeveloped. To determine our proposed model's capability, we apply our proposed SVNHFTS model to three real datasets while also comparing the result to the other FTS models based on improved versions of fuzzy sets. Our datasets include benchmark enrollment data of University of Alabama, IDX Composite (Indonesian composite stock index), and MERVAL index (Argentinian composite stock index). The result shows that our proposed SVNHFTS model outperforms most of the other FTS models in terms of AFE and RMSE, especially the previously proposed NTS model. INDEX TERMS Single-valued neutrosophic hesitant fuzzy set (SVNHFS), single-valued neutrosophic hesitant fuzzy time series (SVNHFTS), neutrosophic time series (NTS), fuzzy time series (FTS).

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