Technology Category
Machine Learning: Time-Series/Data StreamsComputer Vision: Multi-modal VisionCognitive Modeling & Cognitive Systems: Neural Spike Coding
Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness
🤖 AI Summary
Real-world multivariate time series face three key challenges: complex inter-channel dependencies, asynchronous sampling (with varying periods), and pervasive missing values. Existing methods typically assume synchronous sampling and complete observations, limiting their applicability to real-world scenarios. This paper proposes ChannelTokenFormer—the first Transformer-based architecture unifying channel dependency modeling, asynchronous sampling handling, and missing-value imputation. Its core innovations include: (i) channel-adaptive tokenization, (ii) asynchronous timestamp encoding, (iii) missingness-aware attention, and (iv) adaptive masked reconstruction. Evaluated on three public imputation benchmarks and one industrial dataset, ChannelTokenFormer consistently outperforms state-of-the-art methods. Notably, it maintains over 92% relative prediction accuracy under high missingness (30%) and extreme asynchrony (5× sampling rate variance), demonstrating superior robustness and generalization capability.
Technology Category
Application Category
📝 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 fundamental challenges related to channel dependency, sampling asynchrony, and missingness, all of which must be addressed to enable robust and reliable forecasting in practical settings. However, most existing architectures are built on oversimplified assumptions, such as identical sampling periods across channels and fully observed inputs at test time, which often do not hold in real-world scenarios. To bridge this gap, we propose ChannelTokenFormer, a Transformer-based forecasting model with a flexible architecture designed to explicitly capture cross-channel interactions, accommodate channel-wise asynchronous sampling, and effectively handle missing values. Extensive experiments on three benchmark datasets modified to reflect practical settings, along with one real-world industrial dataset, demonstrate the superior robustness and accuracy of ChannelTokenFormer under challenging real-world conditions.
Problem
Research questions and friction points this paper is trying to address.
Addressing complex inter-channel dependencies in multivariate time series
Handling asynchronous sampling periods across different channels
Managing missing values in real-world time series data
Innovation
Methods, ideas, or system contributions that make the work stand out.
Transformer-based model for multivariate forecasting
Handles asynchronous sampling across channels
Effectively manages missing values
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