arXiv:2607. 24892v1 Announce Type: cross Abstract: Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints that the past alone cannot reveal.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints that the past alone cannot reveal. This requires both reliable numerical forecasting and the ability to interpret contextual information.
arXiv:2608. 03031v1 Announce Type: new Abstract: Time series forecasting is fundamental to decision-making in complex systems, where future dynamics are influenced not only by historical observations but also by evolving contextual features.
arXiv:2602. 03164v2 Announce Type: replace-cross Abstract: Time series forecasting (TSF) plays a critical role in decision-making for many real-world applications.
arXiv:2508. 09904v3 Announce Type: replace-cross Abstract: Real-world forecasting requires models to integrate not only historical data but also relevant contextual information provided in textual form.
arXiv:2608. 01875v1 Announce Type: cross Abstract: Most time series (TS) models are specialized for a single task, either understanding (i.
arXiv:2506. 09171v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly capable, but LLM agents still struggle to plan effectively in interactive, partially observable, long-horizon environments when search is unguided or recent history is insufficient.