Diagnosing Similarity of Oscillation Trends in Time Series

Sensor networks have increased the amount and variety of temporal data available, requiring the definition of new techniques for data mining. Related research typically addresses the problems of indexing, clustering, classification, summarization, and anomaly detection. They present many ways for describing and comparing time series, but they focus on their values. This paper concentrates on a new aspect - that of describing oscillation patterns. It presents a technique for time series similarity search, based on multiple temporal scales, defining a descriptor that uses the angular coefficients from a linear segmentation of the curve that represents the evolution of the analyzed series. Preliminary experiments with real datasets showed that our approach correctly characterizes the oscillation of time series.

• Type: Conference
• Year: 2007
• Where: ICDM
• Authors: Leonardo E. Mariote, Claudia Bauzer Medeiros, Ricardo da Silva Torres
• Keywords: Data Mining, ICDM 2007, Multiple Temporal Scales, Time Series, Time Series Similarity