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A UCL paper proposes MAPS, a model-agnostic metric for quantifying the intrinsic predictability of multivariable time series forecasting tasks, evaluated against 66 baseline measures on synthetic and real-world datasets with strong correlation to reference predictability (r=0.963).
This paper proposes a tail-aware geometry learning framework for conformal ellipsoids that decouples tail sensitivity from coverage guarantees, improving uncertainty quantification in multivariate settings.
ChorusTIC is a training-free foundation model for multivariate time series classification that uses in-context learning to handle heterogeneous channel configurations without target-task updates, demonstrating strong performance on standard benchmarks.
This paper proposes DeMTS, a framework that treats denoising trajectories in diffusion language models as multivariate time series to improve hallucination detection, outperforming existing methods with strong robustness and efficiency.
This paper evaluates different multivariate outlier detection methods (Z-score, Mahalanobis distances, PCA, Isolation Forest, and Hotelling's T-squared) for identifying irregular operations in district heating system data, proposing an ensemble approach based on agreement among the best-performing methods.
A discussion thread on multivariate probability models in machine learning.
This blog post explores the intersection of machine learning and neuroscience, specifically focusing on using multivariate classification techniques on neuroimaging data to understand brain function and behavior.