Learning Leading Indicators for Time Series Predictions
This work addresses forecasting in time series analysis for domains like finance or economics, but it is incremental as it builds on existing VAR and Granger-causality frameworks.
The paper tackles the problem of forecasting multiple time series by learning models that discover leading indicators as predictors, using linear vector autoregressive models linked to sparse Granger-causality graphs. It proposes two new methods, including one that uncovers model clusters, and shows they outperform state-of-the-art sparse VAR and graphical Granger learning methods in experiments.
We consider the problem of learning models for forecasting multiple time-series systems together with discovering the leading indicators that serve as good predictors for the system. We model the systems by linear vector autoregressive models (VAR) and link the discovery of leading indicators to inferring sparse graphs of Granger-causality. We propose new problem formulations and develop two new methods to learn such models, gradually increasing the complexity of assumptions and approaches. While the first method assumes common structures across the whole system, our second method uncovers model clusters based on the Granger-causality and leading indicators together with learning the model parameters. We study the performance of our methods on a comprehensive set of experiments and confirm their efficacy and their advantages over state-of-the-art sparse VAR and graphical Granger learning methods.