---
title: "StreamMTS: Towards Streaming Multivariate Time Series Forecasting"
authors:
  - "Binwu Wang"
  - "Jiaming Ma"
  - "Yudong Zhang"
  - "Pengkun Wang"
  - "Zhengyang Zhou"
  - "Xu Wang"
  - "Yang Wang"
doi: "10.24963/ijcai.2026/340"
url: "https://ijcai.org/proceedings/2026/340"
pdf: "https://ijcai.org/proceedings/2026/0340.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "3061-3069"
track: "Main Track"
keywords:
  - "Data Mining: Mining spatial and/or temporal data"
---

# StreamMTS: Towards Streaming Multivariate Time Series Forecasting

Binwu Wang, Jiaming Ma, Yudong Zhang, Pengkun Wang, Zhengyang Zhou, Xu Wang, Yang Wang

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 3061-3069. DOI: [10.24963/ijcai.2026/340](https://doi.org/10.24963/ijcai.2026/340)

## Abstract

Current mainstream research in multivariate time series (MTS) forecasting often assumes that all data are static. However, real-world MTS data typically arrives continuously in a streaming manner, which we refer to as streaming MTS. The statistical characteristics and spatiotemporal graph topology of these data evolve over time, presenting two key challenges: the model's ability to adapt to new data distributions and the enhancement of cross-domain generalization capabilities. In this paper, we propose a streaming MTS prediction framework. We begin by designing a lightweight spatiotemporal causal learning model that captures generalizable causal spatiotemporal features from a decoupling perspective. Next, we introduce a framework to enhance the model's streaming learning capability, leveraging the adaptability of continual learning while strengthening cross-domain representation abilities. Specifically, we reformulate continual learning as a multi-task learning problem and present a multi-task optimization algorithm that identifies a set of Pareto-optimal solutions to address the inherent stability-plasticity dilemma in continual learning. Finally, we propose a topology-aware feature propagation strategy that disseminates well-trained node embedding features to unseen graph structures, thereby improving the model's cross-domain generalization. Results on real-world datasets demonstrate that our model achieves superior forecasting performance while substantially improving computational efficiency and memory usage.