multivariate-time-series

CoRe proposes a model-agnostic learning objective for multivariate time-series forecasting that uses frequency coherence and relational graph losses to improve prediction accuracy over standard methods.

This paper proposes a zero-shot learning framework for multivariate IoT traffic anomaly detection using adversarial and contrastive learning within a variational autoencoder, enabling domain adaptation without labeled data and demonstrating strong performance across diverse datasets.

DynG-Diff proposes a state-aware dynamic guidance diffusion framework for probabilistic multivariate time series forecasting, improving robustness by adaptively handling variable heterogeneity.

Introduces FreSH, a frequency-segmented hierarchical multi-expert framework for multivariate time series classification, achieving state-of-the-art accuracy on UEA benchmarks with reduced model size and computational cost.

Proposes a knowledge-assisted multi-graph framework for multivariate time series anomaly detection in multi-stage industrial processes, incorporating sensor group and process flow knowledge to enhance graph neural network-based dependency modeling.

Proposes a novel framework combining active learning with masked reconstruction and minimax strategies to improve unsupervised time series anomaly detection, achieving 12.39% AUC improvement over baselines across 28 test cases.

STARIXNet is a lightweight neural network that improves cloud resource allocation by capturing multivariate spatio-temporal relationships among system metrics, prioritizing service stability over forecast accuracy. Deployed at Walmart, it achieved 10-50% cost savings while maintaining service reliability.

Proposes CALAD, a channel-aware contrastive learning framework for multivariate time series anomaly detection that uses estimated channel relevance to construct contrastive samples, achieving state-of-the-art performance.