Dual Hesitant Fuzzy Set-Based Intuitionistic Fuzzy Time Series Forecasting

Bisht, Kamlesh; Joshi, Dheeraj Kumar; Kumar, Sanjay

doi:10.1007/978-981-10-7386-1_28

Cited by 14 publications

(9 citation statements)

References 29 publications

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“…• The proposed SVNHFTS model makes sure that there is always a forecasted value in each of the time interval, unlike some of the other compared models, such as Kumar & Gangwar's IFTS (intuitionistic fuzzy time series) [43] and Bisht, et al's DHFS based IFTS [45], where there can be a possibility of no available forecasted value during the defuzzification process.…”

Section: Comparative Studies Of Characteristics Of Svnhfts Model mentioning

confidence: 99%

“…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%

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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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Section: Comparative Studies Of Characteristics Of Svnhfts Model mentioning

confidence: 99%

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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“…Fuzzy sets and rough sets are widely used to solve uncertain problems [1][2][3][4]. However, all these theories have their own deficiency, such as in a voting, you may support, not support, be neutral, or abstain from voting, so Smarandache present the definition of the neutrosophic set (NS) [5].…”

Section: Introductionmentioning

confidence: 99%

Non-Dual Multi-Granulation Neutrosophic Rough Set with Applications

Zhang

Shao

2019

Symmetry

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Multi-attribute decision-making (MADM) is a part of management decision-making and an important branch of the modern decision theory and method. MADM focuses on the decision problem of discrete and finite decision schemes. Uncertain MADM is an extension and development of classical multi-attribute decision making theory. When the attribute value of MADM is shown by neutrosophic number, that is, the attribute value is complex data and needs three values to express, it is called the MADM problem in which the attribute values are neutrosophic numbers. However, in practical MADM problems, to minimize errors in individual decision making, we need to consider the ideas of many people and synthesize their opinions. Therefore, it is of great significance to study the method of attribute information aggregation. In this paper, we proposed a new theory—non-dual multi-granulation neutrosophic rough set (MS)—to aggregate multiple attribute information and solve a multi-attribute group decision-making (MGDM) problem where the attribute values are neutrosophic numbers. First, we defined two kinds of non-dual MS models, intersection-type MS and union-type MS. Additionally, their properties are studied. Then the relationships between MS, non-dual MS, neutrosophic rough set (NRS) based on neutrosophic intersection (union) relationship, and NRS based on neutrosophic transitive closure relation of union relationship are outlined, and a figure is given to show them directly. Finally, the definition of non-dual MS on two universes is given and we use it to solve a MGDM problem with a neutrosophic number as the attribute value.

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“…Many theories have been applied to solve problems with imprecision and uncertainty. Fuzzy set (FS) theories [1][2][3] use the degree of membership to solve the fuzziness. Rough set (RS) theories [4][5][6][7] deal with uncertainty by lower and upper approximation (LUA).…”

Section: Introductionmentioning

confidence: 99%

New Multigranulation Neutrosophic Rough Set with Applications

Zhang

Shao

et al. 2018

Symmetry

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After the neutrosophic set (NS) was proposed, NS was used in many uncertainty problems. The single-valued neutrosophic set (SVNS) is a special case of NS that can be used to solve real-word problems. This paper mainly studies multigranulation neutrosophic rough sets (MNRSs) and their applications in multi-attribute group decision-making. Firstly, the existing definition of neutrosophic rough set (we call it type-I neutrosophic rough set (NRSI) in this paper) is analyzed, and then the definition of type-II neutrosophic rough set (NRSII), which is similar to NRSI, is given and its properties are studied. Secondly, a type-III neutrosophic rough set (NRSIII) is proposed and its differences from NRSI and NRSII are provided. Thirdly, single granulation NRSs are extended to multigranulation NRSs, and the type-I multigranulation neutrosophic rough set (MNRSI) is studied. The type-II multigranulation neutrosophic rough set (MNRSII) and type-III multigranulation neutrosophic rough set (MNRSIII) are proposed and their different properties are outlined. We found that the three kinds of MNRSs generate tcorresponding NRSs when all the NRs are the same. Finally, MNRSIII in two universes is proposed and an algorithm for decision-making based on MNRSIII is provided. A car ranking example is studied to explain the application of the proposed model.

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Dual Hesitant Fuzzy Set-Based Intuitionistic Fuzzy Time Series Forecasting

Cited by 14 publications

References 29 publications

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

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

Non-Dual Multi-Granulation Neutrosophic Rough Set with Applications

New Multigranulation Neutrosophic Rough Set with Applications

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