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We consider the problem of learning models for forecasting multipletime-series systems together with discovering the leading indicators that serveas good predictors for the system. We model the systems by linear vectorautoregressive models (VAR) and link the discovery of leading indicators toinferring sparse graphs of Granger-causality. We propose new problemformulations and develop two new methods to learn such models, graduallyincreasing the complexity of assumptions and approaches. While the first methodassumes common structures across the whole system, our second method uncoversmodel clusters based on the Granger-causality and leading indicators togetherwith learning the model parameters. We study the performance of our methods ona comprehensive set of experiments and confirm their efficacy and theiradvantages over state-of-the-art sparse VAR and graphical Granger learningmethods.

Learning Leading Indicators for Time Series Predictions