SpADe: On Shape-based Pattern Detection in Streaming Time Series

Monitoring predefined patterns in streaming time series is useful to applications such as trend-related analysis, sensor networks and video surveillance. Most current studies on such monitoring employ Euclidean distance to calculate the similarities between given query patterns and subsequences of streaming time series. Euclidean distance has been shown to be ineffective in measuring distances of time series in which shifting and scaling usually exist. Consequently, warping distances such as dynamic time warping (DTW), longest common subsequence (LCSS), have been proposed to handle warps in temporal dimension. However, they are inadequate in handling shifting and scaling in amplitude dimension. Moreover, they have been designed mainly for full sequence matching, whereas in online monitoring applications, we typically have no knowledge on the positions and lengths of possible matching subsequences. In this paper, we first discuss the weaknesses of existing warping distances on detectin...

• Type: Conference
• Year: 2007
• Where: ICDE
• Authors: Yueguo Chen, Mario A. Nascimento, Beng Chin Ooi, Anthony K. H. Tung
• Keywords: Continuous Pattern Detection, Database, Employ Euclidean Distance, ICDE 2007, Time Series