Online Data Mining for Co-Evolving Time Sequences B.-K. Yi, N. Sidiropoulos, T. Johnson, H. Jagadish, C. Faloutsos, and A. Biliris   View Paper (PDF)   Return to Time Series Abstract In many applications, the data of interest comprises multiple sequences that evolve over time. Examples include currency exchange rates, 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 datamining,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 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 algorithmto 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. DiSC'01 Copyright ©2002 ACM Inc.