Fuzzy time series forecasting method based on hesitant fuzzy sets

Bisht, Kamlesh; Kumar, Sanjay

doi:10.1016/j.eswa.2016.07.044

Cited by 105 publications

(60 citation statements)

References 38 publications

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“…Moreover, since most of the IFS-based [16,18] and HFSbased [17,19] models are experimented on the dataset of State Bank of India (SBI) at BSE, we compared our model with other models by using the market prices of SBI share at BSE. As the experiment in [19], we took one sample per month as testing sample, and others as training samples.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Compute Δw and Δb for all weights and offsets (17) Update network weights and offsets (18) End for (19) End for (20) Until RMSE is small enough or fallen into a local minimum ALGORITHM 1: Learning algorithm of the proposed model.…”

Section: Outputmentioning

confidence: 99%

“…However, considering the instability of the data sources and the unreliability of data collecting process, most of the collecting data contain incomplete, imprecise, and ambiguous records, which makes preprocessing an indispensable procedure for machine learning. us, forecasting methods based on fuzzy time series have been proposed to cope with uncertainties caused by vagueness, ambiguity, and other nonprobabilistic reasons and widely applied in finance domain, such as Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) forecasting [3][4][5][6][7][8][9][10][11], the NTD/USD exchange rates forecasting [6,8], and the market price of shares of the State Bank of India (SBI) at the Bombay Stock Exchange (BSE) forecasting [12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Recently intuitionistic fuzzy set (IFS) [27] and hesitant fuzzy set (HFS) [28] have gained attention of researchers [16,29] and proposed a fuzzy time series forecasting model based on intuitionistic fuzzy logical relations, respectively. Bisht et al [17,30] explained fuzzy time series forecasting models built by hesitant fuzzy logical relations to address issue of aforesaid nonstochastic hesitation. Gupta and Kumar [19] proposed an aggregation operator to aggregate hesitant probabilistic fuzzy elements to fuzzy elements.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

Zhang

Wang

et al. 2020

Mathematical Problems in Engineering

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Time series forecasting models based on a linear relationship model show great performance. However, these models cannot handle the the data that are incomplete, imprecise, and ambiguous as the interval-based fuzzy time series models since the process of fuzzification is abandoned. This article proposes a novel fuzzy time series forecasting model based on multiple linear regression and time series clustering for forecasting market prices. The proposed model employs a preprocessing to transform the set of fuzzy high-order time series into a set of high-order time series, with synthetic minority oversampling technique. After that, a high-order time series clustering algorithm based on the multiple linear regression model is proposed to cluster dataset of fuzzy time series and to build the linear regression model for each cluster. Then, we make forecasting by calculating the weighted sum of linear regression models’ results. Also, a learning algorithm is proposed to train the whole model, which applies artificial neural network to learn the weights of linear models. The interval-based fuzzification ensures the capability to deal with the uncertainties, and linear model and artificial neural network enable the proposed model to learn both of linear and nonlinear characteristics. The experiment results show that the proposed model improves the average forecasting accuracy rate and is more suitable for dealing with these uncertainties.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Outputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

Zhang

Wang

et al. 2020

Mathematical Problems in Engineering

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“…The Average Based Fuzzy Time Series method has been widely used as a tool to provide predictions in various purposes. For example the use of method is forecasting inbound tourism demand to Istanbul [1], to the tourism demand forecasting in China [2], forecasting tourist arrivals [3] and many more [4], [5]. The method in this study is Based Fuzzy Time Series which is a simple calculation method that has a simple structure.…”

Section: Introductionmentioning

confidence: 99%

The Prediction Of Tourist Visiting With Average Based Fuzzy Time Series Method

Widians,

Puspitasari,

Sari

2019

IJEAT

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This research aims to build a system which able to predict tourist visiting in the Province of East Kalimantan with Average Based Fuzzy Time Series method. Data collection in this research conducted by direct observation and interview with the Secretary on Duty of the East Kalimantan Province Tourism Department. This research uses the total numbers of tourist that visited the Province of East Kalimantan from 2008 through the year 2016 to predict tourist visit has the following years. In this research, value of MAPE is used to count the value of error or the imprecision of prediction result, so that the result of prediction will reach close to. The result of the building system is the prediction result for international tourist visiting with the MAPE value of 0,77375%, which is included in the criteria of ‘very good’, therefore it can be concluded that this system can be used to predict tourist visiting the Province of East Kalimantan.

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Stock index forecasting: A new fuzzy time series forecasting method

Huang

Wang

et al. 2020

Journal of Forecasting

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This paper presents a new fuzzy time series forecasting model based on technical analysis, affinity propagation (AP) clustering, and a support vector regression (SVR) model. Technical analysis indicators are divided into three categories to construct multivariate fuzzy logical relationships. AP clustering without specifying the number of clusters is used to obtain a suitable partition for the universe of discourse, and the representative exemplars are generated as defuzzied values. The SVR model is employed to explore the unrecognized relationships and modify the forecasts. In addition, the error-based evaluation criteria are applied to evaluate the methods. The performance of the method is evaluated using the Taiwan Capitalization Weighted Stock Index (TAIEX), Standard & Poor's 500 Index (S&P500), and Dow Jones Industrial Average (DJIA) dataset, and the experimental results demonstrate that the proposed method outperforms some classic models.

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Fuzzy time series forecasting method based on hesitant fuzzy sets

Cited by 105 publications

References 38 publications

A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

The Prediction Of Tourist Visiting With Average Based Fuzzy Time Series Method

Stock index forecasting: A new fuzzy time series forecasting method

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