Tóm tắt:

The study follows the hedge algebras (HA-) approach to the 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-variable word-set directlyand, 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-domainbecomes potentially infinite to positively utilize the HA-approach formalism for increasing the LTSforecasting result exactness. Because the forecasting model proposed in this study can directly handleL-words, the LTS, constructed from the numeric time series and its L-relationship groups, consideredhuman knowledges of the given time-series variation helpful for the human-machine interface. Thestudy shows that the proposed formalism can more easily handle the LTS forecasting models andincrease their performance compared to the FTS forecasting models when the words’ number grows.