{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:28:18Z","timestamp":1777696098668,"version":"3.51.4"},"reference-count":47,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2021,10,29]]},"abstract":"<jats:p>Analyzing the temporal behaviors and revealing the hidden rules of objects that produce time series data to detect the events that users are interested in have recently received a large amount of attention. Generally, in various application scenarios and most research works, the equal interval sampling of a time series is a requirement. However, this requirement is difficult to guarantee because of the presence of sampling errors in most situations. In this paper, a multigranularity event detection method for an unequal interval time series, called SSED (self-adaptive segmenting based event detection), is proposed. First, in view of the trend features of a time series, a self-adaptive segmenting algorithm is proposed to divide a time series into unfixed-length segmentations based on the trends. Then, by clustering the segmentations and mapping the clusters to different identical symbols, a symbol sequence is built. Finally, based on unfixed-length segmentations, the multigranularity events in the discrete symbol sequence are detected using a tree structure. The SSED is compared to two previous methods with ten public datasets. In addition, the SSED is applied to the public transport systems in Xiamen, China, using bus-speed time-series data. The experimental results show that the SSED can achieve higher efficiency and accuracy than existing algorithms.<\/jats:p>","DOI":"10.3233\/ida-205480","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T16:06:09Z","timestamp":1635869169000},"page":"1407-1429","source":"Crossref","is-referenced-by-count":2,"title":["Detecting a multigranularity event in an unequal interval time series based on self-adaptive segmenting"],"prefix":"10.1177","volume":"25","author":[{"given":"Haibo","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongbo","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-205480_ref1","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.future.2019.01.045","article-title":"Two approaches for synthesizing scalable residential energy consumption 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