Large-Scale Continuous Structure Learning from Time-Series Data

Structure learning is the problem of recovering from data a Directed Acyclic Graph (DAG) of the interactions among variables. By enforcing a differentiable acyclicity constraint on the adjacency matrix of the graph, existing methods solve this problem as an optimization problem and have been recently extended to time-series data. Due to the cubic computational complexity of existing acyclicity constraints, their application is limited to a few variables. In this paper, we introduce svarcosmo, an optimization-based structure learning method for time-series data that builds upon recent developments on unconstrained but provably acyclic models. We empirically show on both simulated and real data that svarcosmo correctly recovers the underlying DAG in significantly less time, enabling optimization-based structure learning on high-dimensional data.