A Unique Computational Method for Constructing Intervals in Fuzzy Time Series Forecasting

Jain, Shilpa; Mathpal, Prakash C.; Bisht, Dinesh C. S.; Singh, Phool

doi:10.2478/cait-2018-0001

Cited by 13 publications

(6 citation statements)

References 24 publications

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“…This method is based on intersection point of angular bisector called incenter of a triangle, where the abscissas of vertices represent the elements of triangular fuzzy number. The ordinate represents their membership grades [28][29][30][31].…”

Section: Proposed Triangular Incenter Ranking Techniquementioning

confidence: 99%

Dichotomized Incenter Fuzzy Triangular Ranking Approach to Optimize Interval Data Based Transportation Problem

Srivastava

Bisht

2018

Cybernetics and Information Technologies

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This research article discusses the problems having flexible demand, supply and cost in range referred as interval data based transportation problems and these cannot be solved directly using available methods. The uncertainty associated with these types of problems motivates authors to tackle it by converting interval to fuzzy numbers. This confront of conversion has been achieved by proposing a dichotomic fuzzification approach followed by a unique triangular incenter ranking approach to optimize interval data based transportation problems. A comparison with existing methods is made with the help of numerical illustrations. The algorithm proposed is found prompt in terms of the number of iteration involved and problem formation. This method is practical to handle the transportation problems not having a single valued data, but data in form of a range.

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Section: Proposed Triangular Incenter Ranking Techniquementioning

confidence: 99%

Dichotomized Incenter Fuzzy Triangular Ranking Approach to Optimize Interval Data Based Transportation Problem

Srivastava

Bisht

2018

Cybernetics and Information Technologies

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“…The neuro-fuzzy approach was successfully implemented to model the air quality prediction system (Lin et al , 2020). Fuzzy time series (FTS) is found to be more suitable over traditional approaches due to its applicability on both crisp and fuzzy datasets (Jain et al , 2018a, b; Goyal and Bisht, 2020a). Also, real-life situations consist of uncertainty and complexity due to which traditional forecasting methods cannot be implemented in such cases but FTS is an influential means to deal with the vagueness and complexity (Goyal and Bisht, 2019, 2020b).…”

Section: Introductionmentioning

confidence: 99%

“…Also, real-life situations consist of uncertainty and complexity due to which traditional forecasting methods cannot be implemented in such cases but FTS is an influential means to deal with the vagueness and complexity (Goyal and Bisht, 2019, 2020b). The selection of intervals in FTS is always a challenging task and researchers used nature-inspired optimization to deal with this (Jain and Bisht, 2015; Jain et al , 2017, 2018a, b; Bisht et al , 2019). Rahman et al (2015) utilized the FTS and ANN for the prediction of air quality.…”

Section: Introductionmentioning

confidence: 99%

Air quality management using genetic algorithm based heuristic fuzzy time series model

Bhagat

Goyal

Bisht

et al. 2021

TQM

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PurposeThe purpose of this paper is to provide a better method for quality management to maintain an essential level of quality in different fields like product quality, service quality, air quality, etc.Design/methodology/approachIn this paper, a hybrid adaptive time-variant fuzzy time series (FTS) model with genetic algorithm (GA) has been applied to predict the air pollution index. Fuzzification of data is optimized by GAs. Heuristic value selection algorithm is used for selecting the window size. Two algorithms are proposed for forecasting. First algorithm is used in training phase to compute forecasted values according to the heuristic value selection algorithm. Thus, obtained sequence of heuristics is used for second algorithm in which forecasted values are selected with the help of defined rules.FindingsThe proposed model is able to predict AQI more accurately when an appropriate heuristic value is chosen for the FTS model. It is tested and evaluated on real time air pollution data of two popular tourism cities of India. In the experimental results, it is observed that the proposed model performs better than the existing models.Practical implicationsThe management and prediction of air quality have become essential in our day-to-day life because air quality affects not only the health of human beings but also the health of monuments. This research predicts the air quality index (AQI) of a place.Originality/valueThe proposed method is an improved version of the adaptive time-variant FTS model. Further, a nature-inspired algorithm has been integrated for the selection and optimization of fuzzy intervals.

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“…These traditional techniques lack when there is uncertainty in the historical data due to which Song and Chissom (1993) proposed the concept of fuzzy time series (FTS). After which FTS has been used in various applications (Bisht et al, 2013;Jain and Bisht, 2015;Jain et al, 2017;Jain et al, 2018;Goyal and Bisht, 2019). Zhang et al (2010) proposed a model to predict the short-term crude oil price using fuzzy time series.…”

Section: Introductionmentioning

confidence: 99%

Sugeno Intuitionistic Fuzzy Generator Based Computational Technique for Crude Oil Price Forecasting

Goyal¹,

Bisht²

2020

Int J Math, Eng, Manag Sci

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Crude oil being a significant source of energy, change of crude oil price can affect the global economy. In this paper, a new approach based on the intuitionistic fuzzy set theory has been implemented to predict the crude oil price. This paper presents the intuitionistic fuzzy time series forecasting algorithm to enhance the efficacy of time series forecasting which includes fuzzy c-means clustering to obtain the optimal cluster centers. Further, a computational technique is proposed for the construction of triangular fuzzy sets and these fuzzy sets are converted to intuitionistic fuzzy sets with the help of Sugeno type intuitionistic fuzzy generator. The popular benchmark dataset of West Texas Intermediate crude oil spot price is used for the validation process. The numerical results when compared with existing methods notify that the proposed method enhances the accuracy of the crude oil price forecasts.

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A Unique Computational Method for Constructing Intervals in Fuzzy Time Series Forecasting

Cited by 13 publications

References 24 publications

Dichotomized Incenter Fuzzy Triangular Ranking Approach to Optimize Interval Data Based Transportation Problem

Dichotomized Incenter Fuzzy Triangular Ranking Approach to Optimize Interval Data Based Transportation Problem

Air quality management using genetic algorithm based heuristic fuzzy time series model

Sugeno Intuitionistic Fuzzy Generator Based Computational Technique for Crude Oil Price Forecasting

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