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Local Correlation Tracking in Time Series
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.
doi:10.1109/icdm.2006.99
dblp:conf/icdm/PapadimitriouSY06
fatcat:a7dfqfxxirconi3sa42zjmjzua