A Novel Fuzzy Time Series Forecasting Model Based on the Hybrid Wolf Pack Algorithm and Ordered Weighted Averaging Aggregation Operator

2020 ◽  
Vol 22 (6) ◽  
pp. 1832-1850
Author(s):  
Sidong Xian ◽  
Tangjin Li ◽  
Yue Cheng
Keyword(s):  
Time Series ◽  
Aggregation Operator ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Weighted Averaging ◽  
Forecasting Model ◽  
Ordered Weighted Averaging ◽  
Model Based

scholarly journals Comparison of fuzzy time series forecasting model based on similarity measure concept with different types of interval length

2020 ◽  
Author(s):  
Nazirah Ramli ◽  
N. Muhammad Farhan Hakim Nik Badrul Alam ◽  
Siti Musleha Ab Mutalib ◽  
Daud Mohamad
Keyword(s):  
Time Series ◽  
Similarity Measure ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Interval Length ◽  
Forecasting Model ◽  
Model Based ◽  
Different Types

scholarly journals A TIME SERIES FORECASTING MODEL BASED ON LINGUISTIC FORECASTING RULES

2021 ◽  
Vol 37 (1) ◽  
pp. 23-42
Author(s):  
Pham Đinh Phong
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Logical Relationship ◽  
Forecasting Models ◽  
Model Based ◽  
Time Invariant ◽  
Fuzzy Logical

The fuzzy time series (FTS) forecasting models have been being studied intensively over the past few years. Most of the researches focus on improving the effectiveness of the FTS forecasting models using time-invariant fuzzy logical relationship groups proposed by Chen et al. In contrast to Chen’s model, a fuzzy set can be repeated in the right-hand side of the fuzzy logical relationship groups of Yu’s model. N. C. Dieu enhanced Yu’s forecasting model by using the time-variant fuzzy logical relationship groups instead of the time-invariant ones. The forecasting models mentioned above partition the historical data into subintervals and assign the fuzzy sets to them by the human expert’s experience. N. D. Hieu et al. proposed a linguistic time series by utilizing the hedge algebras quantification to converse the numerical time series data to the linguistic time series. Similar to the FTS forecasting model, the obtained linguistic time series can define the linguistic, logical relationships which are used to establish the linguistic, logical relationship groups and form a linguistic forecasting model. In this paper, we propose a linguistic time series forecasting model based on the linguistic forecasting rules induced from the linguistic, logical relationships instead of the linguistic, logical relationship groups proposed by N. D. Hieu. The experimental studies using the historical data of the enrollments of University of Alabama observed from 1971 to 1992 and the daily average temperature data observed from June 1996 to September 1996 in Taipei show the outperformance of the proposed forecasting models over the counterpart ones.

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