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[2506.08660] Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness
Summary
This article presents a novel framework, ChannelTokenFormer, for robust multivariate time series forecasting, addressing challenges of dependency, asynchrony, and missing data.
Why It Matters
The research is significant as it tackles common issues in real-world time series forecasting, which are often overlooked by existing models. By proposing a unified framework that accounts for inter-channel dependencies and asynchronous sampling, it enhances the reliability and accuracy of forecasts in practical applications, making it relevant for industries relying on time-sensitive data.
Key Takeaways
- ChannelTokenFormer effectively captures cross-channel interactions.
- The framework accommodates asynchronous sampling of data channels.
- It provides robust handling of missing values in time series data.
- Extensive experiments demonstrate superior accuracy over existing methods.
- The research addresses critical challenges in practical forecasting scenarios.
Computer Science > Machine Learning arXiv:2506.08660 (cs) [Submitted on 10 Jun 2025 (v1), last revised 24 Feb 2026 (this version, v3)] Title:Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness Authors:Jinkwan Jang, Hyungjin Park, Jinmyeong Choi, Taesup Kim View a PDF of the paper titled Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness, by Jinkwan Jang and 3 other authors View PDF HTML (experimental) Abstract:Real-world time series data are inherently multivariate, often exhibiting complex inter-channel dependencies. Each channel is typically sampled at its own period and is prone to missing values due to various practical and operational constraints. These characteristics pose three fundamental challenges involving channel dependency, sampling asynchrony, and missingness, all of which must be addressed simultaneously to enable robust and reliable forecasting in practical settings. However, existing architectures typically address only parts of these challenges in isolation and still rely on simplifying assumptions, leaving unresolved the combined challenges of asynchronous channel sampling, test-time missing blocks, and intricate inter-channel dependencies. To bridge this gap, we propose ChannelTokenFormer, a Transformer-based forecasting framework with a flexible architecture designed to explicitly capture cross-channel...
