EventTSF: Event-Aware Non-Stationary Time Series Forecasting

TL;DR

EventTSF introduces a diffusion-based framework that integrates textual external events with time series data to improve non-stationary forecasting, addressing modality interaction and non-stationarity challenges.

Contribution

It proposes a novel event-aware diffusion model that incorporates textual events into time series forecasting, enhancing performance over existing methods.

Findings

Outperforms 12 baselines on 7 datasets with significant gains.

Achieves 41.3% average improvement in probabilistic forecasting.

Achieves 27.5% average improvement in deterministic forecasting.

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.