On the effect of membership function in a high-order fuzzy time series

2003 ◽  
Author(s):  
Chao-Chih Tsai ◽  
Shun-Jyh Wu ◽  
Wen-Huei Ting
Keyword(s):  
Time Series ◽  
Membership Function ◽  
High Order ◽  
Fuzzy Time Series

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

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.

scholarly journals Forecasting Electricity Consumption Using Fuzzy Time Series

2018 ◽  
Vol 7 (4.30) ◽  
pp. 342
Author(s):  
K.G. Tay ◽  
Y.Y. Choy ◽  
C.C. Chew
Keyword(s):  
Time Series ◽  
Membership Function ◽  
Electricity Consumption ◽  
Fuzzy Time Series ◽  
Infrastructure Development ◽  
Power Load ◽  
Increasing Demand ◽  
Facility Expansion ◽  
Electric Power Load ◽  
Monthly Electricity Consumption

Electricity consumption forecasting is important for effective operation, planning and facility expansion of power system.  Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development.  There is a great development of Universiti Tun Hussein Onn Malaysia (UTHM) infrastructure since its formation in 1993. The development will be accompanied with the increasing demand of electricity.  Hence, there is a need to forecast the UTHM electricity consumption for future decisions on generating electric power, load switching, and infrastructure development. Therefore, in this study, the Fuzzy time series (FTS) with trapezoidal membership function was implemented on the UTHM monthly electricity consumption from January 2011 to December 2017 to forecast January to December 2018 monthly electricity consumption.  The procedure of the FTS and trapezoidal membership function was described together with January data.  FTS is able to forecast UTHM electricity consumption quite well.

scholarly journals High-Order Fuzzy Time Series Model Based on Generalized Fuzzy Logical Relationship

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Wangren Qiu ◽  
Xiaodong Liu ◽  
Hailin Li
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Composite Index ◽  
High Order ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Logical Relationship ◽  
Proposed Model ◽  
The University ◽  
Fuzzy Logical

In view of techniques for constructing high-order fuzzy time series models, there are three methods which are based on advanced algorithms, computational methods, and grouping the fuzzy logical relationships, respectively. The last kind model has been widely applied and researched for the reason that it is easy to be understood by the decision makers. To improve the fuzzy time series forecasting model, this paper presents a novel high-order fuzzy time series models denoted asGTS(M,N)on the basis of generalized fuzzy logical relationships. Firstly, the paper introduces some concepts of the generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the proposed model is implemented in forecasting enrollments of the University of Alabama. As an example of in-depth research, the proposed approach is also applied to forecast the close price of Shanghai Stock Exchange Composite Index. Finally, the effects of the number of orders and hierarchies of fuzzy logical relationships on the forecasting results are discussed.

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