A refined weighted method for forecasting based on type 2 fuzzy time series

Abhishekh,; Gautam, Surendra Singh; Singh, S. R.

doi:10.1080/02286203.2017.1408948

Cited by 21 publications

(3 citation statements)

References 20 publications

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“…Comparing the model [37], the proposed model employs the HA combining with PSO to select the optimal intervals, whereas the model [37] applies the maximum spanning tree based fuzzy clustering for dividing intervals with different lengths in the intuitionistic FTS model. In addition, the proposed model is also given to compare with other models which are presented in [38,6,11,14,15,17,36] under the number of intervals of 14. The forecasting results and MSE values between our model and other models are given in Table 8.…”

Section: Case (1): Forecasted Results Obtained By the Firstorder Ftsmentioning

confidence: 99%

Improved Fuzzy Time Series Forecasting Model Based on Optimal Lengths of Intervals Using Hedge Algebras and Particle Swarm Optimization

Tính¹,

Dieu²,

Duy³

et al. 2021

Adv. sci. technol. eng. syst. j.

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Recently, numerous scholars have suggested fuzzy time series (FTS) models to forecast many different fields. One of the vital issues for high accurate forecasting in FTS model is method of partitioning in Universe of discourse (UoD). In this research, we propose a novel FTS model, which is established by using hedge algebra (HA) and particle swarm optimization (PSO) for forecasting the different problems. In this model, HA is considered an algebraic structure for partitioning the UoD into unequal-size intervals based on optimal parameters which is determined by PSO. After making the intervals with unequallength, the data values of times series on each interval are symbolized by fuzzy sets and then, these fuzzy sets are utilized to make fuzzy relation groups. Lastly, we keep using the PSO to adjust the size of each interval with view to reaching the better accurate prediction rate. The effectiveness of the proposed method is demonstrated on two datasets which are often applied in many studies in literature as enrolments data of the University of Alabama and Car road accident data in Belgium. The obtained results show that the proposed model produces higher accuracy forecasting when compared with the some of the recent FTS prediction models for all orders of model.

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Section: Case (1): Forecasted Results Obtained By the Firstorder Ftsmentioning

confidence: 99%

Improved Fuzzy Time Series Forecasting Model Based on Optimal Lengths of Intervals Using Hedge Algebras and Particle Swarm Optimization

Tính¹,

Dieu²,

Duy³

et al. 2021

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Introductionmentioning

confidence: 99%

A Refined Approach for Forecasting Based on Neutrosophic Time Series

et al. 2019

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This research introduces a neutrosophic forecasting approach based on neutrosophic time series (NTS). Historical data can be transformed into neutrosophic time series data to determine their truth, indeterminacy and falsity functions. The basis for the neutrosophication process is the score and accuracy functions of historical data. In addition, neutrosophic logical relationship groups (NLRGs) are determined and a deneutrosophication method for NTS is presented. The objective of this research is to suggest an idea of first-and high-order NTS. By comparing our approach with other approaches, we conclude that the suggested approach of forecasting gets better results compared to the other existing approaches of fuzzy, intuitionistic fuzzy, and neutrosophic time series.

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“…He uses the concept to make FLRGs of type 1 fuzzy time series model and after he extended in type 2 fuzzy time series model by defining an extra variables as high and low and used union and intersection operators on type 2 fuzzy set in defuzzification process. Later, Abhishekh et al 26 introduced a weighted type 2 fuzzy time series forecasting method to enhance in the forecasted output. Moreover, Lertworaprachaya et al 27 and Bajestani and Zare 28 introduced a novel improved model for type 2 fuzzy time series forecasting using high order and fourth order fuzzy time series for TAIEX forecasting.…”

Section: Introductionmentioning

confidence: 99%

A New Type 2 Fuzzy Time Series Forecasting Model Based on Three-Factors Fuzzy Logical Relationships

Abhishekh

Gautam

Singh

2019

Int. J. Unc. Fuzz. Knowl. Based Syst.

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The study of fuzzy time series models have been extensively used to improve the accuracy rates in forecasting problems. In this paper, we present a new type 2 fuzzy time series forecasting model based on three-factors fuzzy logical relationship groups. The proposed method uses a new technique to define partitions the universe of discourse into different length of intervals for different factors. Also, the proposed method fuzzifies the historical data sets of the main factors, second factors and third factors to their maximum membership grades obtained by their corresponding triangular fuzzy sets and construct the fuzzy logical relationship groups which is based on the three-factors to enhance in the forecasting accuracy rates. This paper introduces a new defuzzification technique based on their frequency occurrences of fuzzy logical relationships in fuzzy logical relationship groups. The fitness of the propose method is verified in the forecasting of Bombay Stock Exchange (BSE) Sensex historical data and compare in terms of root mean square and average forecasting errors which indicates that the proposed method produce more accurate forecasted output over the existing models in fuzzy time series.

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A refined weighted method for forecasting based on type 2 fuzzy time series

Cited by 21 publications

References 20 publications

Improved Fuzzy Time Series Forecasting Model Based on Optimal Lengths of Intervals Using Hedge Algebras and Particle Swarm Optimization

Improved Fuzzy Time Series Forecasting Model Based on Optimal Lengths of Intervals Using Hedge Algebras and Particle Swarm Optimization

A Refined Approach for Forecasting Based on Neutrosophic Time Series

A New Type 2 Fuzzy Time Series Forecasting Model Based on Three-Factors Fuzzy Logical Relationships

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