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 ad- dresses the problems of indexing, clustering, classification, summarization, and anomaly detection. They present many ways for describing and comparing time series, but they fo- cus on their values. This paper concentrates on a new as- pect - that of describing oscillation patterns. It presents a technique for time series similarity search, based on multi- ple temporal scales, defining a descriptor that uses the an- gular coefficients from a linear segmentation of the curve that represents the evolution of the analyzed series. Prelim- inary experiments with real datasets showed that our ap- proach correctly characterizes the oscillation of time series.