---
title: "EVENTTSF: Event-Aware Non-Stationary Time Series Forecasting"
authors:
  - "Yunfeng Ge"
  - "Ming Jin"
  - "Yiji Zhao"
  - "Hongyan Li"
  - "Bo Du"
  - "Chang Xu"
  - "Shirui Pan"
doi: "10.24963/ijcai.2026/480"
url: "https://ijcai.org/proceedings/2026/480"
pdf: "https://ijcai.org/proceedings/2026/0480.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "4313-4321"
track: "Main Track"
keywords:
  - "Machine Learning: Time series and data streams"
---

# EVENTTSF: Event-Aware Non-Stationary Time Series Forecasting

Yunfeng Ge, Ming Jin, Yiji Zhao, Hongyan Li, Bo Du, Chang Xu, Shirui Pan

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 4313-4321. DOI: [10.24963/ijcai.2026/480](https://doi.org/10.24963/ijcai.2026/480)

## Abstract

Time series forecasting is vital in diverse sectors such as energy and transportation, where non-stationary dynamics are deeply intertwined with external events in other modalities such as texts. However, incorporating natural language-based external events to improve non-stationary forecasting remains largely unexplored, as most approaches still rely on a single modality, resulting in limited contextual knowledge and model underperformance. Enabling fine-grained multimodal interactions between temporal and textual data is challenged by two fundamental issues: (1) the gap in modeling interactions among discrete external events and continuous time series in a unified framework; (2) classical uniform diffusion timestep ignores event-induced non-stationary variability, leading to imbalanced denoising difficulty across diffusion stages. In this work, we propose event-aware non-stationary time series forecasting EventTSF, an autoregressive diffusion framework that integrates historical time series and textual events via step-wise diffusion. To mitigate the imbalanced denoising difficulty of uniform timestep sampling, EventTSF uses an event-aware flow-matching timestep conditioned on event semantics. Extensive experiments on 7 synthetic and real-world datasets show that EventTSF outperforms 12 non-stationary time series forecasting baselines, achieving average gains of 41.3% in probabilistic forecasting and 27.5% in deterministic forecasting across all evaluation metrics.