arXiv Machine Learning

Amortized Interventional Forecasting for Multivariate CIR Processes

arXiv:2608. 03715v1 Announce Type: new Abstract: Mean-reverting dynamics are pervasive in finance, and the Cox--Ingersoll--Ross (CIR) process is a standard model for the time series they produce, from short rates to credit default swap (CDS) spreads.

arXiv Machine Learning
Jul 31

DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series

arXiv:2607. 27263v1 Announce Type: new Abstract: Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimation under-served exactly where it matters most, such as in healthcare, policy evaluation, and climate science.

By Dennis Thumm, Billy Tim Anthony, Ying Chen
arXiv AI
Jun 16

Prediction Bottlenecks Don't Discover Causal Structure (But Here's What They Actually Do)

arXiv:2605. 09169v2 Announce Type: replace-cross Abstract: A Mamba state-space model trained only for next-step prediction appears to recover Granger-causal structure through a simple readout $S = |W_{out} W_{in}|$, with early experiments suggesting the phenomenon generalized across architectures and benefited from interventional data at $p < 10^{-5}$.

By Ankit Hemant Lade, Sai Krishna Jasti, Indar Kumar, Aman Chadha
arXiv Machine Learning
Jul 7

Granger Causality in Extremes

arXiv:2407. 09632v3 Announce Type: replace-cross Abstract: We introduce a rigorous mathematical framework for Granger causality in extremes, designed to identify causal links from extreme events in time series.

By Juraj Bodik, Olivier C. Pasche
arXiv Machine Learning
Jun 9

Forward-Looking Stress Testing Under Macro Scenarios: Stable SVaR Estimation Using a Hybrid GPR-HS Framework with SACS

arXiv:2606. 07575v1 Announce Type: cross Abstract: Regulatory stress testing frameworks, including the Comprehensive Capital Analysis and Review (CCAR) and the Internal Capital Adequacy Assessment Process (ICAAP), require robust Stressed Value-at-Risk (SVaR) estimation under forward-looking macroeconomic scenarios.

By Ujjwala Vadrevu
arXiv Machine Learning
Jul 21

Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables

arXiv:2603. 15802v2 Announce Type: replace Abstract: In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load; calendar and holiday indicators for traffic or sales; and grid load or fuel costs in electricity pricing.

By Andres Potapczynski, Ravi Kiran Selvam, Tatiana Konstantinova, Malcolm Wolff, Kin G. Olivares, Ruijun Ma, Michael W. Mahoney, Andrew Gordon Wilson, Boris N. Oreshkin, Dmitry Efimov