Computer Science Department
School of Computer Science, Carnegie Mellon University
Online Data Mining for Co-Evolving Time Sequences
Byoung-Kee Yi, N.D. Sidiropoulos, Theodore Johnson,
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 so that it works well with limited main memory size and does not cause excessive I/O operations. To 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.