A Score Function-Based Method of Forecasting Using Intuitionistic Fuzzy Time Series

Abstract

Intuitionistic fuzzy set plays a vital role in data analysis and decision-making problems. In this paper, we propose an enhanced and versatile method of forecasting using the concept of intuitionistic fuzzy time series (FTS) based on their score function. The developed method has been presented in the form of simple computational steps of forecasting instead of complicated max–min compositions operator of intuitionistic fuzzy sets to compute the relational matrix [Formula: see text]. Also, the proposed method is based on the maximum score and minimum accuracy function of intuitionistic fuzzy numbers (IFNs) to fuzzify the historical time series data. Further intuitionistic fuzzy logical relationship groups are defined and also provide a forecasted value and lies in an interval and is more appropriate rather than a crisp value. Furthermore, the proposed method has been implemented on the historical student enrollments data of University of Alabama and obtains the forecasted values which have been compared with the existing methods to show its superiority. The suitability of the proposed model has also been examined to forecast the movement of share market price of State Bank of India (SBI) at Bombay Stock Exchange (BSE). The results of the comparison of MSE and MAPE indicate that the proposed method produces more accurate forecasting results.

Multiple attribute group decision‐making based on order‐α divergence and entropy measures under q‐rung orthopair fuzzy environment

“…Hence based on results mentioned in Equations (8) to (12), we conclude ≥ D P Q δ P Q ( | ) 0 ( = 1, 2) with equality if and only = . * α q δ (13) (ii) It has already been proved that ≥ D P Q ( | ) 0 * α q δ (δ = 1, 2), so we must show that the maximum value attains by the divergence measures D P Q ( | ) * α q δ is 1.…”

“…Afterward, several extensions/generalization of FSs have been introduced to solve many real‐world decision problems in different areas. Intuitionistic fuzzy sets (IFSs), proposed by Atanassov, 2 has become one of the extensively studied and used generalizations of FSs in the past three decades 3–12 . In the intuitionistic fuzzy theory, the membership grade of each element is presented by a pair of values in between 0 and 1, in which the first component of each pair represents the membership value, and the second component denotes the nonmembership value of the corresponding element to IFS.…”

“…Intuitionistic fuzzy sets (IFSs), proposed by Atanassov, 2 has become one of the extensively studied and used generalizations of FSs in the past three decades. [3][4][5][6][7][8][9][10][11][12] In the intuitionistic fuzzy theory, the membership grade of each element is presented by a pair of values in between 0 and 1, in which the first component of each pair represents the membership value, and the second component denotes the nonmembership value of the corresponding element to IFS. The primary condition to use IFS theory is that the sum of the values of each pair should be less than or equal to 1.…”

A Refined Approach for Forecasting Based on Neutrosophic Time Series

“…In this example, the suggested approach is implemented on the benchmarking time series data of student enrollments at the University of Alabama from year 1971 to 1992 adopted from [26]. The steps are as follows:…”

“…A heuristic adaptive-order IFTS forecasting model was presented by Wang et al [24]. Subsequently, Abhishekh et al [25,26] presented a weighted type 2 FTS and score function-based IFTS forecasting approach. Moreover, Abhishekh and Kumar [27] suggested an approach for forecasting rice production in the area of FTS.…”