Learning Signals and Graphs from Time-Series Graph Data with Few Causes

Panagiotis Misiakos; Vedran Mihal; Markus Püschel

ICASSP 2024accepted0 citations

Learning Signals and Graphs from Time-Series Graph Data with Few Causes

Panagiotis Misiakos, Vedran Mihal, Markus Püschel

Abstract

In this paper we port assumptions and techniques from DAG (directed acyclic graph) learning and causal inference to time-series graph data. In particular, we view such data as indexed by a DAG obtained by unrolling the graph in time and generated by a causal linear structural equation model (SEM) from only few causes. For this situation we solve two problems: (1) learning the time series from samples, and (2) learning the graph from time-series data by first learning the entire DAG and then extracting the result. We empirically evaluate our approach targeting the few-causes assumption on both synthetic and real-world data and show significant improvements over prior methods.

BibTeX

@inproceedings{icassp2024_learningsignalsa,
  title = {Learning Signals and Graphs from Time-Series Graph Data with Few Causes},
  author = {Panagiotis Misiakos and Vedran Mihal and Markus Püschel},
  booktitle = {ICASSP 2024},
  year = {2024}
}