DYNOTEARS: Structure Learning from Time-Series Data

By Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Paul Beaumont, Konstantinos Georgatzis, Bryon Aragam. International Conference on Artificial Intelligence and Statistics, 2020

311 citations recorded by the source corpus.

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. We also apply this algorithm on real datasets from two different domains, finance and molecular biology, and analyze the resulting output. Compared to state-of-the-art methods for learning dynamic Bayesian networks, our method is both scalable and accurate on real data. The simple formulation and competitive performance of our method make it suitable for a variety of problems where one seeks to learn connections between variables across time.

Subfields: Artificial Intelligence, Molecular Biology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/09630c68d15457cfc1c5d6b6da7f7c639f57e3e6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Molecular Biology

In Signal Processing

Citations in the corpus

D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, background): “CMS [152] 2014 low no Bi NO TEARS [267] 2018 low yes no DAG CGNN [75] 2018 low yes no DAG Graphite [83] 2019 low/medium no no UG SAM [122] 2019 low/medium yes no DAG DAG-GNN [262] 2019 low yes no DAG GAE [177] 2019 low yes no DAG NO BEARS [142] 2019 low/medium/high yes no DAG Meta-Transfer [19] 2019”
DiBS: Differentiable Bayesian Structure Learning (2021, background): “Follow-up work has leveraged this insight to model nonlinear relationships [24–27], time-series data [28], in the context of generative modeling [29, 30], for causal inference [31–33], and contributed to its theoretical understanding [34, 35].”