Handling higher order time series forecasting approach in intuitionistic fuzzy environment

Abhishekh,; Kumar, Sanjay

doi:10.1080/23307706.2019.1591310

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…They also adopted the model to make forecasts about the stock index of SBI. Abhishekh and Kumar [16] proposed a new highorder IFTS model by transforming FTS data into intuitionistic FTS data by defining their appropriate membership and nonmembership grades. The fuzzification of time series data is intuitionistic fuzzification, which is based on the maximum score degree of intuitionistic fuzzy numbers.…”

Section: Definition 23mentioning

confidence: 99%

“…This step calculates the intuitionistic defuzzified values of linguistic values using Equation (15). Then, the forecasting value of t + 1 (i.e., F(t + 1)) can be calculated by Equation 17according to Equation (16).…”

Section: Proposed Ifts Modelmentioning

confidence: 99%

“…After the work by Castillo et al [15], an increasing number of studies in recent years have extended FTS to IFTS [16][17][18][19][20]. However, most of these studies focused on improving prediction results and did not consider uncertainty or hesitation when facing forecasting problems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Intuitionistic Fuzzy Time Series Model Based on New Data Transformation Method

Chen¹,

Chen²,

Chang³

et al. 2021

IJCIS

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Traditional time series methods can predict seasonal problems, but not problems with transferred linguistic data. Thus, a forecasting method for such problems is required. However, existing intuitionistic fuzzy time series forecasting methods lack persuasiveness in determining the degree of hesitation and the lengths of intervals. Hence, this research is mainly to explore how to decide the degree of hesitation for each interval for intuitionistic fuzzy time series. This paper proposes the weighted intuitionistic fuzzy time series model based on the Nth quantile discretization approach (NQDA). The proposed model can decide the appropriate number, interval length, degree of hesitation, and membership and nonmembership functions of linguistic values on the basis of the training data. In the experimental section, the forecasts of several data sets are made for model validation. Results indicate that the proposed model can be used to obtain forecasts for other time-related data sets.

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Section: Definition 23mentioning

confidence: 99%

Section: Proposed Ifts Modelmentioning

confidence: 99%