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A time series forecasting model based on linguistic forecasting rules / Pham, Dinh Phong // Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics . - 2021. - tr. 25-44. - ISSN: 1813-9663
Ký hiệu phân loại (DDC): 006
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.
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Scalable Human Knowledge About Numeric Time Series Variation And Its Role In Improving Forecasting Results / Nguyen Huy Hieu, Nguyen Cat Ho, Pham Dinh Phong..[and others] // Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics . - 2022. - tr. 3-30. - ISSN: 1813-9663
Ký hiệu phân loại (DDC): 005.1
Instead of handling fuzzy sets associated with linguistic (L-) laltmis asset the devel—opers’ intuition immediately, the study follows the hedge algebras (HA-) approach to Lie- time seriesforecasting problems, in which the linguistic time series forecasting model was. for the first time.proposed and examined in 2020. It can handle the declared forecasting L—variabie word-set directly and, hence, the terminology linguistic time-series (LTS) is used instead of the fuzzy time-series (FTS). Instead of utilizing a limited number of fuzzy sets, this study views the L-variable under considera - tion as to the numeric forecasting variable’s human linguistic counterpart. Hence. its word—domain becomes potentially infinite to positively utilize the HA-approach formalism for increasing the LTS forecasting result exactness. Because the forecasting model proposed in this study can directly handle L—words. the LTS, constructed from the numeric time series and its L-relationship groups. Considered human knowledges of the given time—series variation helpful for the human-machine interface. The study shows that the proposed formalism can more easily handle the LTS forecasting models and increase their performance compared to the FTS forecasting models when the words‘ munher grows.
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