arXiv:2608. 10149v1 Announce Type: new Abstract: Due to the diversity of real-world time series, no single forecasting model consistently dominates across all samples.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2506. 10630v3 Announce Type: replace-cross Abstract: To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techniques to data-driven deep learning architectures.
arXiv:2506.10630v4 Announce Type: replace-cross Abstract: To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techni...
arXiv:2510. 03519v3 Announce Type: replace-cross Abstract: Time series reasoning is crucial to decision-making in diverse domains, including finance, energy, and scientific discovery.
NeST is a framework that adapts large language models (LLMs) for continuous time‑series forecasting by creating neighborhood‑aware text prototypes and aligning them with temporal representations through a nearest‑neighbor contrastive objective. It retrieves the most relevant prototypes and uses them to conditionally modulate time‑series features, enabling more effective integration of textual and temporal information. Experiments show that NeST outperforms state‑of‑the‑art methods on eight benchmarks, reduces MSE by 1.2% for long‑term forecasting, improves zero‑shot forecasting by 4.9%, and boosts R² by 3.3% on a real‑world photovoltaic power forecasting task.
arXiv:2508. 07195v2 Announce Type: replace-cross Abstract: Recent advances have demonstrated that Large Language Models (LLMs) can be effectively adapted for time series forecasting, revealing strong potential beyond natural language tasks.
arXiv:2609.40265v1 Announce Type: new Abstract: Real-world time-series applications increasingly require models that can handle time series forecasting, context-conditioned prediction, and language-b...