@inproceedings{wu-etal-2026-predictive,
title = "Predictive Language Grounding for Event-Driven Time Series Forecasting via Dual-Path Event Injection",
author = "Wu, Jintao and
Xue, Xin and
Cao, Yabin and
QI, Peng and
Zhao, Su and
Chen, Shuai and
Lin, Wei and
Chen, Tianyu and
Zhou, Haoyi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1764/",
pages = "37728--37745",
ISBN = "979-8-89176-462-0",
abstract = "Discrete events such as holidays, policy announcements, and public gatherings leave no trace in historical time series yet fundamentally shape future demand. Language models capture precisely this event knowledge, making them a natural source of auxiliary signal for event-driven forecasting. However, some multimodal forecasters borrow semantic alignment from vision-language pretraining, enforcing geometric proximity between text and series embeddings. For time series regression, this objective is theoretically unnecessary and actively harmful when training data is limited or text-series coupling is weak: contrastive gradients compress the high-variance series features required to represent event-induced spikes. We propose predictive grounding as the principled alternative, shaping language representations through prediction loss alone. PROSE (Predictive grOunding for event-driven time-Series forEcasting) realizes this framework through a fully fine-tuned language encoder, token-level Event Token Injection, LLM-generated Trajectory Reasoning Anchors as global prefix tokens, and a Soft Functional Alignment loss. On a 464K-sample Chinese holiday benchmark, PROSE surpasses the strongest baseline by 10.7{\%} WMAPE; on Time-MMD, it achieves state-of-the-art results across 7 of 9 domains."
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<abstract>Discrete events such as holidays, policy announcements, and public gatherings leave no trace in historical time series yet fundamentally shape future demand. Language models capture precisely this event knowledge, making them a natural source of auxiliary signal for event-driven forecasting. However, some multimodal forecasters borrow semantic alignment from vision-language pretraining, enforcing geometric proximity between text and series embeddings. For time series regression, this objective is theoretically unnecessary and actively harmful when training data is limited or text-series coupling is weak: contrastive gradients compress the high-variance series features required to represent event-induced spikes. We propose predictive grounding as the principled alternative, shaping language representations through prediction loss alone. PROSE (Predictive grOunding for event-driven time-Series forEcasting) realizes this framework through a fully fine-tuned language encoder, token-level Event Token Injection, LLM-generated Trajectory Reasoning Anchors as global prefix tokens, and a Soft Functional Alignment loss. On a 464K-sample Chinese holiday benchmark, PROSE surpasses the strongest baseline by 10.7% WMAPE; on Time-MMD, it achieves state-of-the-art results across 7 of 9 domains.</abstract>
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%0 Conference Proceedings
%T Predictive Language Grounding for Event-Driven Time Series Forecasting via Dual-Path Event Injection
%A Wu, Jintao
%A Xue, Xin
%A Cao, Yabin
%A QI, Peng
%A Zhao, Su
%A Chen, Shuai
%A Lin, Wei
%A Chen, Tianyu
%A Zhou, Haoyi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wu-etal-2026-predictive
%X Discrete events such as holidays, policy announcements, and public gatherings leave no trace in historical time series yet fundamentally shape future demand. Language models capture precisely this event knowledge, making them a natural source of auxiliary signal for event-driven forecasting. However, some multimodal forecasters borrow semantic alignment from vision-language pretraining, enforcing geometric proximity between text and series embeddings. For time series regression, this objective is theoretically unnecessary and actively harmful when training data is limited or text-series coupling is weak: contrastive gradients compress the high-variance series features required to represent event-induced spikes. We propose predictive grounding as the principled alternative, shaping language representations through prediction loss alone. PROSE (Predictive grOunding for event-driven time-Series forEcasting) realizes this framework through a fully fine-tuned language encoder, token-level Event Token Injection, LLM-generated Trajectory Reasoning Anchors as global prefix tokens, and a Soft Functional Alignment loss. On a 464K-sample Chinese holiday benchmark, PROSE surpasses the strongest baseline by 10.7% WMAPE; on Time-MMD, it achieves state-of-the-art results across 7 of 9 domains.
%U https://aclanthology.org/2026.findings-emnlp.1764/
%P 37728-37745
Markdown (Informal)
[Predictive Language Grounding for Event-Driven Time Series Forecasting via Dual-Path Event Injection](https://aclanthology.org/2026.findings-emnlp.1764/) (Wu et al., Findings 2026)
ACL
- Jintao Wu, Xin Xue, Yabin Cao, Peng QI, Su Zhao, Shuai Chen, Wei Lin, Tianyu Chen, and Haoyi Zhou. 2026. Predictive Language Grounding for Event-Driven Time Series Forecasting via Dual-Path Event Injection. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 37728–37745, Budapest, Hungary. Association for Computational Linguistics.