Simulation-free latent SDE inference restricts approximate posteriors and degrades learning, but Helmholtz-SDE closes the gap by optimizing over compatible path laws to match simulation-based accuracy at lower cost.
HyFAD couples time- and frequency-domain diffusion for time series imputation, using frequency-aware step embeddings to improve high-frequency reconstruction and achieve state-of-the-art results.
TSQAgent uses collaborative agent roles and external analytical tools to automatically identify relevant time series quality dimensions and perform quantitative comparisons, substantially improving LLM assessment and downstream data selection.
Time-delay embeddings of periodic signals are homotopy equivalent to circles, enabling TopPT, a hypothesis test with asymptotic error control for detecting periodicity via confidence-bounded persistence diagrams.
ProCTI retrieves global dataset prototypes to augment local conditioning in diffusion-based time series imputation, improving accuracy under sparse or noisy missingness with theoretical guarantees.
TIDES moves input dependence from step size to the state matrix in selective SSMs, preserving physical time steps and per-token expressivity for irregular series, achieving state-of-the-art time-series results.
Constellation-Aware Transformer injects geometric inductive biases into semi-supervised equalization via constellation-aware attention and FIR-inspired filtering, outperforming baselines with fewer pilots.
Online Log-NCDEs use a continuous, injective increment-based embedding to build log-signatures directly from irregular asynchronous observations without interpolation, achieving universal approximation with online efficiency and robustness.
A direct data-adaptive test for Gaussian graphical models in high-dimensional long-memory time series achieves asymptotic size and power consistency via block bootstrap.