Problem
Research questions and friction points this paper is trying to address.
irregular time series
time series classification
interpretability
non-uniform sampling
A Time-Aware Bag-of-Receptive-Fields for Interpretable Irregular Time Series Classification
🤖 AI Summary
This study addresses the challenges that interpolation disrupts temporal structures and deep models lack interpretability in irregular time series classification. To overcome these limitations, this work extends the BORF transform by introducing an interval-based weighted normalization mechanism and a sliding window recursive algorithm. The proposed approach enables feature extraction to be aware of the true temporal distribution while efficiently processing non-uniformly sampled data with linear complexity. By integrating Bag-of-Receptive-Fields techniques, the method achieves state-of-the-art performance on the PYRREGULAR dataset, successfully unifying high classification accuracy with human interpretability.
📝 Abstract
Irregular time series, characterized by non-uniform sampling intervals, missing observations, and variable lengths, are ubiquitous in healthcare, mobility, and environmental monitoring, yet effective and interpretable classifiers for this setting are limited. Existing approaches often rely on imputation, which can obscure the temporal structure of the data, or require complex neural architectures that are opaque and difficult to explain. In this work, we extend the Bag-Of-Receptive-Fields (BORF), a fast, deterministic, and interpretable transform for time series, to the irregular setting. Our key contribution is a time-weighted normalization scheme in which each observation is weighted proportionally to its associated time delta, making pattern extraction sensitive to the actual temporal distribution of samples rather than only their index position. This requires deriving an efficient sliding-window recurrence for the time-weighted standard deviation, preserving the linear time complexity of BORF. We benchmark the resulting method against state-of-the-art irregular time series classifiers on datasets from the PYRREGULAR repository, demonstrating competitive classification performance with the added benefit of human-interpretable explanations.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Irregular Time Series
Bag-of-Receptive-Fields
Time-Aware Normalization
Interpretable Classification
Sliding-Window Recurrence
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