DYNOTEARS: Structure Learning from Time-Series Data

Understand

We revisit the structure learning problem for dynamic Bayesian networks and propose a method that simultaneously estimates contemporaneous (intra-slice) and time-lagged (inter-slice) relationships between variables in a time-series.

• Our approach is score-based, and revolves around minimizing a penalized loss subject to an acyclicity constraint.
• To solve this problem, we leverage a recent algebraic result characterizing the acyclicity constraint as a smooth equality constraint.
• The resulting algorithm, which we call DYNOTEARS, outperforms other methods on simulated data, especially in high-dimensions as the number of variables increases.