Local Correlation Tracking in Time Series

Spiros Papadimitriou, Jimeng Sun, Philip Yu
2006 IEEE International Conference on Data Mining. Proceedings  
We address the problem of capturing and tracking local correlations among time evolving time series. Our approach is based on comparing the local auto-covariance matrices (via their spectral decompositions) of each series and generalizes the notion of linear cross-correlation. In this way, it is possible to concisely capture a wide variety of local patterns or trends. Our method produces a general similarity score, which evolves over time, and accurately reflects the changing relationships.
more » ... lly, it can also be estimated incrementally, in a streaming setting. We demonstrate its usefulness, robustness and efficiency on a wide range of real datasets. Jul
doi:10.1109/icdm.2006.99 dblp:conf/icdm/PapadimitriouSY06 fatcat:a7dfqfxxirconi3sa42zjmjzua