Notice of Retraction: A Novel Forecasting Model of Fuzzy Time Series Based on K-means Clustering

Chi, Kai; Fu, Fang-Ping; Che, Wen-Gang

doi:10.1109/etcs.2010.249

Cited by 18 publications

(5 citation statements)

References 18 publications

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“…This is also a high and low time series. Up to now, there are dozens of fuzzy time series forecasting models proposed by scholars (part of the forecasting models were put forward by [5][6][7][8][9][10][11][12][13]), which HONG-XU WANG and ZHEN-XING WU [5] put forward the set (SD) of forecasting model. When simulate and forecast the enrollment numbers of University of Alabama in 1971~1992, we gain the best time series forecasting models of MSE=0and AFER=0%.…”

Section: Introductionmentioning

confidence: 99%

The Set of Ternary Time Series Forecasting Models Based on the Difference

Feng¹,

Wang²,

Yin³

et al. 2018

dtcse

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Keywords:The difference, The forecasting function Fq(a,b,c) of STD, The Ternary time series forecasting models, The automatic optimal search method.Abstract. The set of ternary time series forecasting models based on difference is proposed (STD). For a time series, it can select the best time series forecasting model in STD by using the automatic optimal search method. For example, when forecast enrollments data of University of Alabama in 1971~1992, can select the best time series forecasting model Fq(0.000004,0.9,0.000004) in STD by using the automatic optimal search method, and can gain the MSE=0 and AFER=0%. Increasing the best time series forecasting models' numbers of STD which forecast accuracy reaches its peak.

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Section: Introductionmentioning

confidence: 99%

The Set of Ternary Time Series Forecasting Models Based on the Difference

Feng¹,

Wang²,

Yin³

et al. 2018

dtcse

View full text Add to dashboard Cite

show abstract

“…These two cases are rule-less time series. So far, the fuzzy time series forecasting model has been put forward (the prediction model mentioned in document [6][7][8][9][10][11][12][13][14][15] is a part of it). In 2009 Stevenson and Porter [6] proposed the forecasting model of Fuzzy time series , when simulate and predict the enrollment number of University of Alabama, get the average prediction error and mean square error AFER=0.57% MSE=21575 [6], although it have been the best prediction accuracy since 2009, but still not too high.…”

Section: Introductionmentioning

confidence: 99%

A Set of Time Series Forecasting Model Based on the Difference

Feng¹,

Wang²,

Yin³

et al. 2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)

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Abstract-A set of time series forecasting models based on difference is proposed (SD). For a time series, it can select the best time series forecasting model in SD by using the automatic optimal search method. For example, when forecast enrollments data of University of Alabama in 1971~1992, it can select the best time series forecasting model Dq(0.000003,0.000003) in SD by using the automatic optimal search method, and can gain the MSE=0 and AFER=0%. The fact that the prediction accuracy of the existing fuzzy time series prediction model is not very high has been fundamentally improved.Keywords-the difference; the function Xq(s,t) of SD's sum of fraction; the inverse function Zq(s,t) of SD's sum of fraction; the prediction function Dq(s,t) of SD; time series

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“…method to filter out the ideal in ASD prediction model Aj (0.000003,0.000003), mean square error MSE=0 and the average prediction error rate of AFER=0%, which obtain satisfactory prediction accuracy. Since then, the fuzzy time series forecasting model are emerging [6][7][8][9][10][11][12][13][14][15]. In year 2007, Jilani & Burney & Ardil [4,5] used the concept of de-fuzzification, proposed a fuzzy time series forecasting model, simulated and predicted of the number of registered at the University of Alabama, obtained AFER=1.0242% MSE=41426 [5],which forecasting accuracy is the best since 2007, but still unsatisfactory.…”

Section: Introductionmentioning

confidence: 99%

A Set of Time Series Prediction Models Based on Difference Method

Lü¹,

Wang²,

Yin³

et al. 2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)

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Abstract-This paper proposed a set of time series prediction models based on difference method(ASD). For a time series, the computer can automatically find the time series search method to filter out the ideal in ASD prediction model. For example, the forecast number of registered at the University of Alabama in 1971~1992 years, the ideal forecasting model is Aj (0.000003,0.000003), which can make the mean square error MSE=0 and the average prediction error rate AFER=0%, that thoroughly solve the unsatisfactory prediction accuracy of the existing fuzzy time series forecasting model. Keywords-differential rate; a set of time series prediction models; ASD's sum of fraction functions Kj (U, V)

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Notice of Retraction: A Novel Forecasting Model of Fuzzy Time Series Based on K-means Clustering

Cited by 18 publications

References 18 publications

The Set of Ternary Time Series Forecasting Models Based on the Difference

The Set of Ternary Time Series Forecasting Models Based on the Difference

A Set of Time Series Forecasting Model Based on the Difference

A Set of Time Series Prediction Models Based on Difference Method

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