Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness
Jinkwan Jang, Hyungjin Park, Jinmyeong Choi, Taesup Kim
TL;DR
This paper introduces ChannelTokenFormer, a Transformer-based framework that effectively models inter-channel dependencies, handles asynchronous sampling, and manages missing data to improve robustness and accuracy in real-world multivariate time series forecasting.
Contribution
The paper presents a unified Transformer-based framework that simultaneously addresses dependency, asynchrony, and missingness in multivariate time series forecasting, a challenge not fully tackled by prior methods.
Findings
Outperforms existing models on benchmark datasets.
Demonstrates robustness under real-world conditions.
Effectively handles missing data and asynchronous sampling.
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…
Peer Reviews
Decision·ICLR 2026 Poster
1. The paper formalizes a comprehensive and realistic forecasting challenge: simultaneous handling of dependency, asynchrony, and missingness, as illustrated clearly in Figure 1, moving beyond isolated treatment of these aspects in prior works. 2. The paper introduces a unified mask-guided attention mechanism for channel tokens (see Figure 2 and Figure 3) that elegantly separates local intra-channel modeling from cross-channel global aggregation, supporting asynchronous and missing inputs natura
1. While practical motivation for the mask-guided attention and frequency-based patching is strong, the formal theoretical analysis is somewhat lacking. For example, the impact of frequency-based patching on generalization is empirically supported but not mathematically justified. 2. The bulk of the validation is empirical, with most arguments for effectiveness given by ablations and performance gains. The absence of principled theoretical results or proofs (e.g., why the particular masking sc
1、Multivariate time series forecasting is important to various domains. 2、There are quite a few nice illustrations. 3、This work focuses on an important problem that could have real-world applications. 4、The figures and tables used in this work are clear and easy to read.
1、The proposed algorithm has notable limitations. The authors should further clarify whether their method can effectively handle missing values during training, especially in scenarios where missing values appear in a discrete rather than continuous manner. It remains unclear how the model ensures robustness and applicability under such conditions. 2、In addition, although the authors claim that their method addresses three key challenges in real-world scenarios—variable modeling, multi-source a
1. The paper tackles a crucial, real-world problem by addressing channel dependency, asynchrony, and missingness in a unified manner, moving beyond the idealized assumptions common in much of the literature. 2. The proposed ChannelTokenFormer, with its unified mask-guided attention, offers an elegant solution that avoids signal-distorting interpolation. The integration of frequency-based dynamic patching is a smart way to handle heterogeneous sampling rates. 3. The paper is exceptionally clear,
1. The authors compare their method with mainly channel-dependent transformer-based methods. However, many methods have achieved SOTA performance with non-transformer architectures [1] [2]. Comparison with these methods is necessary for a comprehensive evaluation. 2. The section introducing the research methodology lacks essential mathematical formulas and specific descriptive details, which creates certain difficulties for readers to fully understand the implementation logic and operational st
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Taxonomy
TopicsTime Series Analysis and Forecasting · Forecasting Techniques and Applications · Stock Market Forecasting Methods