STEPS reformulates test-time adaptation for time series forecasting as a Dirichlet boundary value problem on a temporal manifold and solves for smooth error corrections, yielding 26.82% average relative MSE reduction over zero-shot baselines.
arXiv preprint arXiv:2406.09130 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 3years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
VLBM learns a shared latent basis for stable ID dynamics and orthogonal OOD residuals via variational alignment of future-aware posterior with future-blind prior, reporting 15.08% MAE and 7.74% MSE gains on 12 OOD benchmarks.
A cross-machine anomaly detection framework disentangles MOMENT embeddings using random forests to create machine-invariant condition features that improve generalization to unseen machines on industrial data.
citing papers explorer
-
STEPS: A Temporal Smooth Error Propagation Solver on the Manifolds for Test-Time Adaptation in Time Series Forecasting
STEPS reformulates test-time adaptation for time series forecasting as a Dirichlet boundary value problem on a temporal manifold and solves for smooth error corrections, yielding 26.82% average relative MSE reduction over zero-shot baselines.
-
VLBM: Variational Latent Basis Modeling for OOD Robust Multivariate Time Series Forecasting
VLBM learns a shared latent basis for stable ID dynamics and orthogonal OOD residuals via variational alignment of future-aware posterior with future-blind prior, reporting 15.08% MAE and 7.74% MSE gains on 12 OOD benchmarks.
-
Cross-Machine Anomaly Detection Leveraging Pre-trained Time-series Model
A cross-machine anomaly detection framework disentangles MOMENT embeddings using random forests to create machine-invariant condition features that improve generalization to unseen machines on industrial data.