A novel probabilistic intuitionistic fuzzy set based model for high order fuzzy time series forecasting

Pattanayak, Radha Mohan; Behera, Himansu Sekhar; Panigrahi, Sibarama

doi:10.1016/j.engappai.2020.104136

Cited by 56 publications

(19 citation statements)

References 47 publications

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“…Reduced amounts of RMSE and AFE (Table 17) in forecasting enrollments of the University of Alabama confirm outperformance of the proposed method over the method proposed by Qiu et al (2011), Yolcu et al (2009, Joshi and Kumar (2012), Bisht and Kumar (2016), Kumar and Gangwar (2016), Gupta and Kumar (2019), Iqbal and Zhang (2020), Gautam et al (2020) and Pattanayak et al (2021). However, proposed method underperforms methods of Iqbal and Zhang (2020) and Gautam et al (2020) in terms of error measures but TS values beyond the acceptable range put question on the validity of these models.…”

Section: Performance Analysis Of Proposed Fts Forecasting Methodsmentioning

confidence: 81%

“…Proposed HFS-based hybrid FTS forecasting method also outperforms in forecasting of car sells time series data of Quebec. Its performance is found better than various forecasting methods of Aladag et al (2009), Kumar and Gangwar (2016), Kumar (2016), Bas et al (2018), Gupta and Kumar (2019) and Pattanayak et al (2021) in terms RMSE and AFE (Table 19). Acceptable range of WI and TS also show the perfect match between actual and forecasted market SBI share price and validity of the proposed hybrid FTS forecasting method.…”

Section: Performance Analysis Of Proposed Fts Forecasting Methodsmentioning

confidence: 82%

“…Chen and Kao (2013), Chen et al (2016) and Shuai et al (2017) proposed FTS forecasting model with SVM. Recently, Pattanayak et al (2020Pattanayak et al ( , 2021 proposed high-order FTS forecasting model using probabilistic IFS and SVM.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy time series forecasting based on hesitant fuzzy sets, particle swarm optimization and support vector machine-based hybrid method

Pant

Kumar

2021

Granul. Comput.

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In this paper, we propose hesitant fuzzy sets-based hybrid time series forecasting method using particle swarm optimization and support vector machine. Length of unequal intervals, weights of intervals and process of defuzzification are major factors that affect the forecasting accuracy of hesitant fuzzy sets-based time series models. The proposed hybrid fuzzy time series forecasting method uses hesitant fuzzy sets which are constructed using fuzzy sets with equal and unequal length intervals. Particle swarm optimization and linear programming are used to optimize length of unequal intervals and weights of equal and unequal intervals. The proposed hybrid method of time series forecasting uses support vector machine for setting input-target pattern for defuzzification. Outperformance of proposed hybrid method of time series forecasting method is revealed by applying it on widely used time series data of enrollments of the University of Alabama, market share price of State Bank of India share at Bombay stock exchange and car sell in Quebec City of Canada. Validity of the proposed hybrid fuzzy time series forecasting method is verified using values of Willmott index and tracking signal.

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Section: Performance Analysis Of Proposed Fts Forecasting Methodsmentioning

confidence: 81%

Section: Performance Analysis Of Proposed Fts Forecasting Methodsmentioning

confidence: 82%

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Fuzzy time series forecasting based on hesitant fuzzy sets, particle swarm optimization and support vector machine-based hybrid method

Pant

Kumar

2021

Granul. Comput.

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“…The theory of Fuzzy set is capable of mathematical formulation of many variables, concepts and systems that are imprecise and vague, and can provide for inference, argument and decision making in conditions of uncertainty (Xu, 2014;Chaira, 2019). A generally accepted principle in the theory of classical sets is drawing the boundaries in these sets so that membership and nonmembership of a particular element are determined with absolute certainty (Pattanayak et al, 2021). A characteristic function can define each fuzzy subset A in the reference set X.…”

Section: Fuzzy Sets and Fuzzy Numbermentioning

confidence: 99%

A Fuzzy MADM Approach for Selecting the Best Cyanide Removal Method from a Gold Mine Wastewater

Hayati

Ganji

Shahcheraghi

2021

Preprint

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The cyanidation process is the most common method applied for the extraction of gold and silver in the hydrometallurgy industry, in which, sodium cyanide is used as a leaching agent. Therefore, the wastewater of gold mines contains a wide variety of cyanide ions needing to be removed before these wastewaters can be discharged to the receiving environments. In this study, a fuzzy multi-attribute decision-making approach (Fuzzy Delphi AHP and Fuzzy TOPSIS) was used for selecting the best cyanide removal method from the wastewater of Muteh gold mine. According to the experts' opinion, three methods including calcium hypochlorite, hydrogen peroxide and sodium hypochlorite were selected as alternatives. Then, by introducing the criteria influencing decision making, including cyanide removal ability, cost of process, amount of material consumed, time, pH, ease of performance and safety, and performing separated experiments, the criteria for each of three methods were determined. Finally, sodium hypochlorite was proposed as the best method for eliminating cyanide from wastewater. It was found that the rank of methods was as sodium hypochlorite (0.517) > calcium hypochlorite (0.474) > hydrogen peroxide (0.463).

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“…The TSK fuzzy logic very popular in some research on time-series data [10], control [13][14], modeling [15][16], classification [17]. This model is also widely used for issues related to forecasting, as in the researches that were conducted by Gharlegi et al [15], Sarip et al [18], Du et al [19], Pattanayak et al [20], and Chang et al [21]. Sarip et al [18] used the fuzzy model for real estate price prediction.…”

Section: Introductionmentioning

confidence: 99%

Evolution strategies based coefficient of TSK fuzzy forecasting engine

Sari

Mahmudy

Wibawa

2021

Int. J. Adv. Intell. Informatics

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Forecasting is a method of predicting past and current data, most often by pattern analysis. A Fuzzy Takagi Sugeno Kang (TSK) study can predict Indonesia's inflation rate, yet with too high error. This study proposes an accuracy improvement based on Evolution Strategies (ES), a specific evolutionary algorithm with good performance optimization problems. ES algorithm used to determine the best coefficient values on consequent fuzzy rules. This research uses Bank Indonesia time-series data as in the previous study. ES algorithm uses the popSize test to determine the number of initial chromosomes to produce the best optimal solution for this problem. The increase of popSize creates better fitness value due to the ES's broader search area. The RMSE of ES-TSK is 0.637, which outperforms the baseline approach. This research generally shows that ES may reduce repetitive experiment events due to Fuzzy coefficients' manual setting. The algorithm complexity may cost to the computing time, yet with higher performance.

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A novel probabilistic intuitionistic fuzzy set based model for high order fuzzy time series forecasting

Cited by 56 publications

References 47 publications

Fuzzy time series forecasting based on hesitant fuzzy sets, particle swarm optimization and support vector machine-based hybrid method

Fuzzy time series forecasting based on hesitant fuzzy sets, particle swarm optimization and support vector machine-based hybrid method

A Fuzzy MADM Approach for Selecting the Best Cyanide Removal Method from a Gold Mine Wastewater

Evolution strategies based coefficient of TSK fuzzy forecasting engine

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