arXiv Machine Learning
Sep 18
When Does Retrieval Help Time-Series Forecasting?
arXiv:2609. 20193v1 Announce Type: new Abstract: Retrieval plug-ins supply a deep forecaster with information its lookback window cannot carry.
GNA: Granular Neighbor Assembly for Retrieval-Augmented Multivariate Time-Series Forecasting
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arXiv Machine Learning
Aug 7
Align-RAG: Alignment Is All You Need for TSFM In-Context Learning
arXiv:2608. 05571v1 Announce Type: new Abstract: Retrieval-augmented forecasting promises to adapt frozen Time Series Foundation Models (TSFMs) to new domains without fine-tuning, but recent methods typically rely on learned fusion modules, i.
arXiv Machine Learning
Aug 17
Model-agnostic Retrieval-Augmented Extended Forecasting for time series
arXiv:2608. 14054v1 Announce Type: new Abstract: Time series forecasting with pretrained foundation models has demonstrated strong zero-shot capabilities.
arXiv Statistics ML
2d ago
RACE: Residual-Aware Test-Time Adaptation for Neighbor-Rich Time-Series Foundation Model Forecasting
arXiv:2610.00405v1 Announce Type: cross Abstract: Time-series foundation models (TSFMs) perform strongly across forecasting tasks, but their per-series inference is ill-suited to neighbor-rich foreca...
arXiv Machine Learning
Sep 17
Which Histories Matter for Time Series Forecasting? Learning Predictive Relevance with Future Supervision
The paper investigates which historical examples are most useful for time‑series forecasting by defining predictive relevance as the expected future utility conditioned on inference‑time information. It introduces a two‑stage approach: a normalized‑pattern retriever generates a coarse candidate set, and a lightweight MLP reranks these candidates using future‑supervised relevance while keeping inference strictly past‑only. Experiments on six benchmarks show that this reranker improves pattern retrieval and outperforms a matched‑protocol baseline, revealing that historical relevance is structured, domain‑dependent, and not governed by a single universal retrieval rule.
arXiv Machine Learning
Jun 26
How Good Can Linear Models Be for Time-Series Forecasting?
arXiv:2606. 27282v1 Announce Type: new Abstract: Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation models, on the assumption that capacity is what unlocks accuracy.
arXiv Machine Learning
2d ago
When, Not How Much: Evaluating Time-Series Foundation Models on Sparse Events
arXiv:2609.39386v1 Announce Type: new Abstract: Pretrained time-series foundation models (TSFMs) are evaluated as forecasters of future values, yet for sparse series many decisions depend only on whi...
arXiv AI
Sep 4
RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting
RATL is a plug‑in method for multivariate time‑series forecasting that uses a frozen base forecaster to build a memory of its historical forecast residuals. During inference, RATL retrieves residual trajectories from similar past contexts and employs a set‑aware router to combine them, providing learned feedback correction. Experiments demonstrate that this residual‑retrieval approach improves the performance of the base forecaster across various benchmarks and backbones.
Hugging Face Trending Papers
Jun 25
How Good Can Linear Models Be for Time-Series Forecasting?
Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation models, on the assumption that capacity is what unlocks accuracy. We take the opposite position: most of the gap can be closed at far lower cost by tuning preprocessing rather than scaling models.
arXiv AI
Aug 10
KReF: Training-Free Retrieval for Long-Term Time-Series Forecasting and Predictive Uncertainty
arXiv:2608. 06748v1 Announce Type: cross Abstract: Probabilistic long-term time-series forecasting commonly relies on trained models.
arXiv AI
Jul 8
The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error
arXiv:2607. 05450v1 Announce Type: cross Abstract: This paper explores the "Granularity Paradox" in time-series forecasting, wherein finer temporal disaggregation (e.
arXiv AI
Sep 10
Memory in Deep Time-Series Models
arXiv:2609.06006v1 Announce Type: cross Abstract: Deep learning for time series has progressed through successive architectural paradigms, from recurrent networks and transformers to structured state...