arXiv Machine Learning
PathFM: Fast Generative Forecasting of Multivariate Trajectories
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arXiv Machine Learning
Sep 15
FlowTSFM: Turning Encoder Depth into Quantile Transport
arXiv:2609.13640v1 Announce Type: new Abstract: Encoder-based time series foundation models (TSFMs) typically rely on deep stacks of independently parameterized Transformer layers, where only the fin...
arXiv Machine Learning
Sep 29
Aurora-X: Built for Extreme Time Series Forecasting
Aurora‑X is a billion‑parameter time‑series foundation model designed for extreme forecasting tasks. It employs a progressive curriculum that starts with channel‑independent pretraining, then adds cross‑variable dependencies, variable context and horizon lengths, and optional future covariates during mid‑training. A variable‑resolution post‑training stage allows adjustable temporal spans per token at inference, while a pattern‑guided mixture‑of‑experts expands capacity through sparse activation and expert specialization. An implicit quantile network head predicts arbitrary quantiles, enhancing probabilistic forecasting flexibility. Experiments on GIFT‑Eval, TIME, FEV‑Bench, TFB, and DAG‑Bench show state‑of‑the‑art performance against both pretrained TSFMs and task‑specific supervised models.
arXiv Machine Learning
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Quantile-Guided Diffusion for Probabilistic Time-Series Forecasting
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arXiv Machine Learning
Jul 28
Autoregressive One-Step Generative Modeling for Dynamical System Forecasting
arXiv:2605. 05540v2 Announce Type: replace Abstract: Fast surrogate modeling for high-dimensional physical dynamics requires more than low short-term error: useful models must roll out efficiently while preserving the statistical structure of long trajectories.
Hugging Face Trending Papers
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Deterministic Prediction as Global Velocity: Flow Matching for Probabilistic Time-Series Forecast
The paper introduces DV-Flow, a flow‑matching framework that treats deterministic prediction as a parallel velocity component instead of embedding it within the generative process. This design stabilizes end‑to‑end training and unifies several existing methods as special cases, enabling direct comparison. Experiments on six real‑world datasets show that DV‑Flow consistently outperforms strong baselines in probabilistic time‑series forecasting.
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.