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 ...
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