Temperature prediction using fuzzy time series
| dc.citation.epage | 275 | en_US |
| dc.citation.issue | 2 | en_US |
| dc.citation.spage | 263 | en_US |
| dc.citation.volume | 30 | en_US |
| dc.citation.woscount | 144 | |
| dc.contributor.author | Chen, SM | en_US |
| dc.contributor.author | Hwang, JR | en_US |
| dc.contributor.department | 資訊工程學系 | zh_TW |
| dc.contributor.department | Department of Computer Science | en_US |
| dc.date.accessioned | 2014-12-08T15:45:27Z | |
| dc.date.available | 2014-12-08T15:45:27Z | |
| dc.date.issued | 2000-04-01 | en_US |
| dc.description.abstract | A drawback of traditional forecasting methods is that they can not deal with forecasting problems in which the historical data are represented by linguistic values. Using fuzzy time series to deal with forecasting problems can overcome this drawback. In this paper, we propose a new fuzzy time series model called the two-factors time-variant fuzzy time series model to deal with forecasting problems. Based on the proposed model,,ve develop two algorithms for temperature prediction. Both algorithms have the advantage of obtaining good forecasting results. | en_US |
| dc.identifier.doi | 10.1109/3477.836375 | en_US |
| dc.identifier.issn | 1083-4419 | en_US |
| dc.identifier.journal | IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS | en_US |
| dc.identifier.uri | http://dx.doi.org/10.1109/3477.836375 | en_US |
| dc.identifier.uri | https://ir.lib.nycu.edu.tw/handle/11536/30610 | |
| dc.identifier.wosnumber | WOS:000086532400002 | |
| dc.language.iso | en_US | en_US |
| dc.subject | main-factor fuzzy time series | en_US |
| dc.subject | second-factor fuzzy time series | en_US |
| dc.subject | temperature prediction | en_US |
| dc.subject | time-invariant fuzzy time series | en_US |
| dc.subject | time-variant fuzzy time series | en_US |
| dc.title | Temperature prediction using fuzzy time series | en_US |
| dc.type | Article | en_US |