arXiv Machine Learning
StaFIR: Convex Learning of Stationarity-Aware Causal Filters
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
Hugging Face Trending Papers
Oct 7
DSTNet: Dynamic Spectral Trajectory Network for Causal Multi-Horizon Financial Forecasting
Wavelet-based financial forecasters typically use the transform only to denoise, or reduce it to a single spectral snapshot at the forecast origin, and the convolution that produces the coefficients i...
arXiv Machine Learning
Oct 5
Causal Representation Learning with Instantaneous and Lagged Relations via Nonstationarity
The paper introduces iCReN, a framework for causal representation learning in time‑series data that jointly models instantaneous and lagged causal relations while handling nonstationarity. It establishes theoretical conditions for identifying latent states and their causal structures up to permutation and component‑wise invertible transformations, using an auxiliary variable linked to changes in transition‑noise distributions. Experiments show that iCReN accurately recovers latent states and both types of causal relations on synthetic data and improves downstream forecasting on real‑world data.
arXiv Machine Learning
Oct 8
DSTNet: Dynamic Spectral Trajectory Network for Causal Multi-Horizon Financial Forecasting
arXiv:2610.09654v1 Announce Type: new Abstract: Wavelet-based financial forecasters typically use the transform only to denoise, or reduce it to a single spectral snapshot at the forecast origin, and...
arXiv AI
Jun 2
Regime-Adaptive Continual Learning for Portfolio Management
arXiv:2606. 00143v1 Announce Type: cross Abstract: Financial markets are inherently non-stationary, exhibiting frequent regime shifts and structural changes that render traditional Portfolio Management (PM) approaches ineffective.
arXiv Machine Learning
Jun 3
FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Finance
arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.
arXiv Machine Learning
Jun 9
The Label Horizon Paradox: Rethinking Supervision Targets in Financial Forecasting
arXiv:2602. 03395v4 Announce Type: replace Abstract: While deep learning has revolutionized financial forecasting through sophisticated architectures, the design of the supervision signal itself is rarely scrutinized.