Class-Conditioned Gaussian Mixture Modeling for Imbalanced Time Series Quantification
CC-GMNet-TS is a novel quantification model that estimates class prevalences in imbalanced time series data by combining a Transformer-based feature extractor with per-class Gaussian mixture models in a bounded latent space Unlike prior mixture-based quantifiers that share a single Gaussian mixture across all classes, CC-GMNet-TS assigns each class its own compact mixture, enabling better capture of rare but informative patterns The model uses the Artificial Prevalence Protocol (APP) and prior s
Analysis
TL;DR
- CC-GMNet-TS is a novel quantification model that estimates class prevalences in imbalanced time series data by combining a Transformer-based feature extractor with per-class Gaussian mixture models in a bounded latent space
- Unlike prior mixture-based quantifiers that share a single Gaussian mixture across all classes, CC-GMNet-TS assigns each class its own compact mixture, enabling better capture of rare but informative patterns
- The model uses the Artificial Prevalence Protocol (APP) and prior shift bag sampling (PShift) to construct training bags covering diverse class prevalence scenarios, trained end-to-end with a quantification-oriented loss
- Experiments on EMG Data for Gestures, SmartFallMM, and UCI-HAR benchmarks demonstrate lower error compared to traditional aggregators and recent deep quantifiers
- Ablation studies confirm that both the Transformer backbone and class-conditioned mixtures contribute significantly to performance gains under prior shift conditions
Why It Matters
This work addresses a critical gap in time series analysis where aggregate class prevalence estimation matters more than individual instance labels—common in healthcare monitoring, activity recognition, and biosignal processing. By tackling the challenging setting of imbalanced data with prior shift, the model provides practitioners with a robust tool for real-world deployment where class distributions are rarely uniform.
Technical Details
- Architecture: CC-GMNet-TS integrates a Transformer-based feature extractor with class-conditioned Gaussian mixture models, where each class has its own compact mixture in a bounded latent space rather than sharing a single mixture
- Bag Construction: Uses the Artificial Prevalence Protocol (APP) and prior shift bag sampling (PShift) to generate training bags spanning a wide range of class prevalence scenarios, simulating real-world distribution shifts
- Training: End-to-end optimization with a quantification-oriented loss function specifically designed for prevalence estimation rather than classification accuracy
- Benchmarks: Evaluated on three time series datasets—EMG Data for Gestures, SmartFallMM (fall detection), and UCI-HAR (human activity recognition)—all featuring imbalanced class distributions
- Ablation Findings: Both the Transformer backbone and class-conditioned mixture components were shown to be essential, with the class-specific mixtures providing particular benefits under prior shift conditions
Industry Insight
- The class-conditioned mixture approach offers a principled way to handle rare classes in time series quantification, which is directly applicable to healthcare monitoring systems where critical events (e.g., falls, anomalies) are inherently rare
- The use of APP and PShift for bag construction provides a reproducible framework for evaluating quantification models under realistic prior shift conditions, encouraging the community to adopt more rigorous evaluation protocols
- As time series data from wearables and IoT devices grows, quantification models that handle imbalance and distribution shift will become increasingly valuable for population-level analytics in smart health and activity monitoring applications
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