Online Data Mining for Co-Evolving Time Sequences

摘要

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: (a) estimation/forecasting of missing/delayed/future values, (b) quantitative data mining, discovering correlations (with or without lag) among the given sequences, 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 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.