Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factors high-order fuzzy time series

In view of techniques for constructing high-order fuzzy time series models, there are three types which are based on advanced algorithms, computational method, and grouping the fuzzy logical relationships. The last type of models is easy to be understood by the decision maker who does not know anything about fuzzy set theory or advanced algorithms. To deal with forecasting problems, this paper presented novel high-order fuzz time series models denoted as GTS(M, N)based on generalized fuzzy logical relationships and automatic clustering. This paper issued the concept of generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the procedure of the proposed model was implemented on forecasting enrollment data at the University of Alabama. To show the considerable outperforming results, the proposed approach was also applied to forecasting the Shanghai Stock Exchange Composite Index. Finally, the effects of parametersMandN, the number of order, and concerned principal fuzzy logical relationships, on the forecasting results were also discussed.

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Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony

Granular Computing ◽

10.1007/s41066-018-00143-5 ◽

2018 ◽

Vol 4 (4) ◽

pp. 639-654 ◽

Cited By ~ 6

Author(s):

Erol Egrioglu ◽

Ufuk Yolcu ◽

Eren Bas

Keyword(s):

Neural Networks ◽

Time Series ◽

Artificial Neural Networks ◽

Artificial Bee Colony ◽

Time Series Forecasting ◽

High Order ◽

Fuzzy Time Series ◽

Bee Colony ◽

Forecasting Method ◽

Artificial Neural

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A novel data partitioning and rule selection technique for modeling high-order fuzzy time series

Applied Soft Computing ◽

10.1016/j.asoc.2017.11.011 ◽

2018 ◽

Vol 63 ◽

pp. 87-96 ◽

Cited By ~ 17

Author(s):

Mahua Bose ◽

Kalyani Mali

Keyword(s):

Time Series ◽

Data Partitioning ◽

High Order ◽

Fuzzy Time Series ◽

Selection Technique ◽

Rule Selection

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

Mathematical Problems in Engineering ◽

10.1155/2020/9546792 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Yanpeng Zhang ◽

Hua Qu ◽

Weipeng Wang ◽

Jihong Zhao

Keyword(s):

Time Series ◽

Linear Regression ◽

Multiple Linear Regression ◽

Time Series Forecasting ◽

High Order ◽

Fuzzy Time Series ◽

Forecasting Model ◽

Time Series Clustering ◽

Proposed Model ◽

High Order Time

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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