Perceptual Indexing of Multivariate Time Series

We consider the problem of deriving compressed perceptual representation of multivariate time series and using it for efficient indexing and similarity search. Our algorithm is based on the identification of perceptual skeletons in multidimensional space and the use of these "simplifications" in similarity measurements. We illustrate the performance of the algorithm in a financial modeling application. Our results indicate that the skeleton representation outperforms the traditional approaches and is robust enough to be used even with the simplest distance metrics.