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.COMS E6125 Web-enHanced Information Management (WHIM) Prof. Experiments on real datasets show that MUSCLES outperforms popular competitors in prediction accuracy up to 10 times, and discovers interesting correlations. 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. It can handle indefinitely long sequences efficiently using an incremental algorithm and requires only small amount of storage and less I/O operations. Our method, MUSCLES, adapts to changing correlations among time sequences. 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. Examples include currency exchange rates, network traffic data. 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.ĪB - In many applications, the data of interest comprises multiple sequences that evolve over time. N2 - In many applications, the data of interest comprises multiple sequences that evolve over time. T1 - Online data mining for co-evolving time sequences 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.", 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.Ībstract = "In many applications, the data of interest comprises multiple sequences that evolve over time.
In many applications, the data of interest comprises multiple sequences that evolve over time.
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