fast correlation analysis on time series datasets
There has been increasing interest for efficient techniques for fast correlation analysis of time series data in different application domains. We present three algorithms for (1) bivariate correlation queries, (2) multivariate correlation queries, and (3) correlation queries based on a new correlation measure we introduce using dynamic time warping. To support these algorithms, we use a variant of the Compact Multi-Resolution Index (CMRI). In addition to conventional nearest neighbor and range queries supported by CMRI, the proposed algorithms compute all answers to user-defined, ad hoc and parametric correlation queries. The results of our experiments indicate a speed-up of two orders of magnitude over the brute force algorithm, and an order of magnitude improvement on average, while offering more functionalities than provided by existing techniques such as StatStream and the Spatial Cone Tree.
correlation analysis time series multi-resolution index dynamically time warped correlation