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    Nhan đề: A time series forecasting model based on linguistic forecasting rules /
DDC 006
Tác giả CN Pham, Dinh Phong
Tác giả TT
Nhan đề A time series forecasting model based on linguistic forecasting rules / Pham, Dinh Phong
Tóm tắt The fuzzy time series (FTS) forecasting models have been sturlied intesively over the pizza-nabpast few years. The existing FTS forecasting models partition the historical data into and assign the fuzzy sets to them by the human expert‘s experience. Hieu et al. propo time series by utilizing the hedge algebras quantification to converse the numerical . Data to the linguistic time series. Similar to the FTS forecasting models, the obtained Ii:- time series can define the linguistic, logical relationships which are used to establish the 32,1; legit relationship groups and form a linguistic forecasting model. In this paper. we propose a 5;- fistu- time series forecasting model based on the linguistic forecasting rules induced from the ; logical relationships instead of the linguistic, logical relationship groups proposed by Hieu. T1»:— experimental studies using the historical data of the enrollments of University of Alabama and the iii; average temperature data in Taipei Show the outperformance of the proposed forecasting Elsi-1‘ over thecounterpart ones. Then, to realize the proposed models in Viet Nam. they are sis; apple-d to thefei'eeasliug problem of the historical data of the average rice production of Viet .from 1990 to 2010.
Từ khóa tự do Defuzzification
Từ khóa tự do Linguistic logical relationship
Từ khóa tự do Linguistic time series
Từ khóa tự do Hedge algebras
Nguồn trích Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics 2021tr. 25-44 Số: 01
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039|y20240918145120|ztainguyendientu
040 |aACTVN
041 |avie
044 |avm
082 |a006
10010|aPham, Dinh Phong
110 |bVietnam Academy Of Science And Technology
245 |aA time series forecasting model based on linguistic forecasting rules / |cPham, Dinh Phong
520 |a The fuzzy time series (FTS) forecasting models have been sturlied intesively over the pizza-nabpast few years. The existing FTS forecasting models partition the historical data into and assign the fuzzy sets to them by the human expert‘s experience. Hieu et al. propo time series by utilizing the hedge algebras quantification to converse the numerical . Data to the linguistic time series. Similar to the FTS forecasting models, the obtained Ii:- time series can define the linguistic, logical relationships which are used to establish the 32,1; legit relationship groups and form a linguistic forecasting model. In this paper. we propose a 5;- fistu- time series forecasting model based on the linguistic forecasting rules induced from the ; logical relationships instead of the linguistic, logical relationship groups proposed by Hieu. T1»:— experimental studies using the historical data of the enrollments of University of Alabama and the iii; average temperature data in Taipei Show the outperformance of the proposed forecasting Elsi-1‘ over thecounterpart ones. Then, to realize the proposed models in Viet Nam. they are sis; apple-d to thefei'eeasliug problem of the historical data of the average rice production of Viet .from 1990 to 2010.
653 |aDefuzzification
653 |aLinguistic logical relationship
653 |aLinguistic time series
653 |aHedge algebras
7730 |tTạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics |d2021|gtr. 25-44|x1813-9663|i01
890|a0|b0|c1|d0
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