Date of Original Version
©2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Abstract or Description
In many applications, the data of interest comprises multiple sequences that evolve over time. Examples include currency exchange rates and network traffic data. We develop a fast method to analyze such co-evolving time sequences jointly to allow (a) estimation/forecasting of missing/delayed/future values, (b) quantitative data mining, and (c) outlier detection. Our method, MUSCLES, adapts to changing correlations among time sequences. It can handle indefinitely long sequences efficiently using an incremental algorithm and requires only a small amount of storage and less I/O operations. To make it scale for a large number of sequences, we present a variation, the Selective MUSCLES method and propose an efficient algorithm to reduce the problem size. Experiments on real datasets show that MUSCLES outperforms popular competitors in prediction accuracy up to 10 times, and discovers interesting correlations. Moreover, Selective MUSCLES scales up very well for large numbers of sequences, reducing response time up to 110 times over MUSCLES, and sometimes even improves the prediction quality
Data Engineering, 2000. Proceedings. 16th International Conference on, 13-22.