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  • Bài trích
  • Ký hiệu PL/XG: 005.1
    Nhan đề: Scalable Human Knowledge About Numeric Time Series Variation And Its Role In Improving Forecasting Results /
DDC 005.1
Tác giả CN Nguyen, Huy Hieu
Nhan đề 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]
Tóm tắt 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.
Từ khóa tự do Linguistic logical relationship
Từ khóa tự do Linguistic time series
Từ khóa tự do Hedge algebras: Quantitative
Tác giả(bs) CN Pham, Dinh Phong
Tác giả(bs) CN Nguyen, Cat Ho
Nguồn trích Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics 2022tr. 3-30 Số: 02
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10010|aNguyen, Huy Hieu
245 |aScalable Human Knowledge About Numeric Time Series Variation And Its Role In Improving Forecasting Results / |cNguyen Huy Hieu, Nguyen Cat Ho, Pham Dinh Phong..[and others]
520 |aInstead 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.
653 |aLinguistic logical relationship
653 |aLinguistic time series
653 |aHedge algebras: Quantitative
700 |aPham, Dinh Phong
700 |aNguyen, Cat Ho
7730 |tTạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics |d2022|gtr. 3-30|x1813-9663|i02
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