TLDR: Glocal-IB is a new training method for time series imputation that addresses the “optimization dilemma” of existing models failing under high missing rates. It introduces a Global Alignment loss to the Information Bottleneck framework, enabling models to capture both local details and global data structure, leading to significantly improved imputation accuracy and stable data representations with minimal computational overhead.
Time Series Imputation (TSI) is a crucial task in many fields, from healthcare to transportation, where missing data points in sequences of observations are common due to various issues like sensor malfunctions or data collection errors. The challenge lies in accurately filling these gaps, especially when a large portion of the data is missing. Traditional methods often struggle in these high-missingness scenarios, leading to poor predictions and distorted underlying data representations.
Researchers have identified a significant problem: existing models, while performing well during training, often fail during the actual imputation process when a lot of data is missing. This happens because they tend to focus too much on reconstructing individual data points (local information) and end up “memorizing” noise rather than understanding the broader patterns and overall structure of the data (global information). This leads to a critical “optimization dilemma” where low training error doesn’t guarantee good real-world performance.
Introducing Glocal Information Bottleneck
To tackle this, a new training approach called Glocal Information Bottleneck (Glocal-IB) has been proposed by Jie Yang, Kexin Zhang, Guibin Zhang, Philip S. Yu, and Kaize Ding. This method is designed to help models capture both the fine-grained local details and the overarching global structure of time series data, even when faced with significant missingness. Glocal-IB builds upon the well-known Information Bottleneck (IB) principle, which aims to create data representations that are compact (removing noise) yet informative (keeping relevant data).
What makes Glocal-IB unique is the introduction of a “Global Alignment loss.” This innovative component ensures that the learned representations of incomplete data are closely aligned with those of their complete, original counterparts. By doing so, the model is encouraged to preserve the essential global characteristics and temporal dependencies of the time series, rather than getting sidetracked by local noise. This alignment is achieved with a surprisingly simple addition: a single, lightweight Multilayer Perceptron (MLP), making Glocal-IB easy to integrate into existing time series imputation models.
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Experimental Validation and Efficiency
Extensive experiments were conducted across nine diverse time series datasets, including those from electricity, weather, and traffic. The results consistently showed that Glocal-IB significantly improves imputation performance. For instance, on several datasets, it reduced errors (measured by MSE) by up to 40% compared to other leading methods. Even in challenging scenarios with up to 90% missing data, Glocal-IB helped models maintain stable and coherent data representations, whereas other models showed severe distortion or collapse in their internal data structures.
The research also explored the generality and efficiency of Glocal-IB. It was found to enhance the capabilities of various existing imputation models, such as TimesNet and SAITS, across different missing data patterns, including challenging “block-wise” missingness where large contiguous segments of data are absent. Furthermore, Glocal-IB proved to be computationally efficient, adding only a marginal increase in memory and execution time, unlike some other alignment methods that rely on large “foundation models” and incur substantial overhead.
An important finding from the study is that both the “Regularization loss” (which helps suppress noise) and the “Global Alignment loss” (which ensures global semantic consistency) are crucial for Glocal-IB’s effectiveness. While a small amount of regularization is beneficial, too much can suppress useful information. The Global Alignment loss, however, consistently helps the model learn better representations. This work offers a promising direction for developing more robust and accurate time series imputation models for real-world applications. For more technical details, you can refer to the full research paper.
