multivariate

Google introduces TimesFM-3, a state-of-the-art zero-shot foundation model for multivariate time series forecasting, capable of handling multiple targets and covariates in a single forward pass without fine-tuning.

This paper identifies an objective gap in Direct Forecasting for multivariate time series and proposes CvLoss, a plug-in structural regularizer that constrains forecast residuals on a cross-variable graph to improve consistency across synchronous and asynchronous interactions. Experiments show consistent improvements over competitive forecasting models.

This paper proposes a novel framework for causal discovery in multivariate time series using inverted self-attention mechanisms, introducing a causal self-attention module (CSAM) and a global causal algorithm to identify latent causal links and reduce spurious correlations.

CARNet integrates global recurrent cycle information into efficient core-based interaction modeling for multivariate time series forecasting, achieving linear complexity and outperforming strong transformer baselines on real-world benchmarks.

Datadog released Toto-2.0, a time series model that scales like LLMs, featuring Contiguous Patch Masking and Arcsinh normalization for improved forecasting.

Introduces RMISC, a large-scale real-world multivariate time series corpus with around 200 datasets and 142 billion time points, and demonstrates that pretraining time series foundation models on real-world multivariate data improves zero-shot generalization compared to synthetic data.

This paper proposes a falsifiable applicability criterion for a training-free, fixed-length descriptor for multivariate time series based on time-lagged spectral embeddings, showing when it can be expected to work and validating it on multiple benchmarks.

ReGeN is a reference-guided generative pipeline for multivariate time series data that decomposes observed sequences into periodic backbone, stochastic residuals, and cross-variable dependencies to synthesize controllable synthetic data. It demonstrates that generated data can substitute for real data in forecasting tasks, outperforming prior synthetic data generators.

This paper introduces a diagnostic framework for multivariate time series anomaly detection benchmarks and finds that labeled anomalies are mostly detectable from individual channels, challenging the need for cross-channel modeling. The authors call for more structurally diverse evaluation sets.

This paper introduces TSCOMP, a large-scale benchmark that systematically decomposes deep multivariate time-series forecasting methods into fine-grained components to enable automated model selection, outperforming complex holistic architectures.