Dòng Nội dung
1
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.
 Số bản sách: (0) Tài liệu số: (1)