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,
H.V. Jagadish, Christos Faloutsos, Alexandros Biliris
October 1999
CMU-CS-99-171.ps
CMU-CS-99-171.pdf
In many applications, the data of interest comprises
multiple sequences that evolve over time. Examples
include currency exchange rates, network traffic data,
and demographic data on multiple variables.
We develop a fast method to analyze such co-evolving
time sequences jointly to allow
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.
24 pages
Return to:
SCS Technical Report Collection This page maintained by reports@cs.cmu.edu
School of Computer Science homepage