mlsanityData to 14 Oct 2026

SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Input

arXiv 2501.03130 ↗First posted 6 Jan 2025cs.LG stat.MLcited by 0

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Abstract (arXiv)

We introduce SpinSVAR, a novel method for estimating a structural vector autoregression (SVAR) from time-series data under sparse input assumption. Unlike prior approaches using Gaussian noise, we model the input as independent Laplacian variables, enforcing sparsity and yielding a maximum likelihood estimator (MLE) based on least absolute error regression. We provide theoretical consistency guarantees for the MLE under mild assumptions. SpinSVAR is efficient: it can leverage GPU acceleration to scale to thousands of nodes. On synthetic data with Laplacian or Bernoulli-uniform inputs, SpinSVAR outperforms state-of-the-art methods in accuracy and runtime. When applied to S&P 500 data, it clusters stocks by sectors and identifies significant structural shocks linked to major price movements, demonstrating the viability of our sparse input assumption.

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References · 22

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  11. 2021Interpretable Models for Granger Causality Using Self-explaining Neural Networkscited by 45
  12. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
10 more
  1. 2021Neural graphical modelling in continuous-time: consistency guarantees and algorithmscited by 42
  2. 2023CUTS+: High-dimensional Causal Discovery from Irregular Time-seriescited by 41
  3. 2014Bayesian linear regression with sparse priorscited by 40
  4. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
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  6. 2022Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noisecited by 30
  7. 2020Neural Additive Vector Autoregression Models for Causal Discovery in Time Seriescited by 28
  8. 2022Causal Fourier Analysis on Directed Acyclic Graphs and Posetscited by 12
  9. 2023Learning DAGs from Data with Few Root Causescited by 8
  10. 2024AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUscited by 4

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