📈 Time Series

🧠 NeurIPS2026 · 11 paper notes

📌 Same area in other venues: 🎞️ ECCV2026 (5) · 📷 CVPR2026 (7) · 🔬 ICLR2026 (121) · 💬 ACL2026 (8) · 🧪 ICML2026 (45) · 🤖 AAAI2026 (31)

🔥 Top topics: Time-Series Forecasting ×9

AdaST: Adaptive Coupling for Spatial-Temporal Forecasting

AdaST decomposes spatial-temporal inputs into temporal-specific, spatial-specific, and jointly coupled representations, models them separately, and adaptively recomposes them with correlation-modulated gates, achieving the best reported results on four short-term sensor forecasting benchmarks and reducing PurpleAir MAE from the strongest comparator's 0.511 to 0.489.

Beyond Empirical Support: Structured Outlier Generation via Sinkhorn Optimal Transport

SBOG uses Sinkhorn support energy to identify latent boundary anchors and selects locally perturbed candidates with weak support but controlled semantics, raising CARLA's average pointwise F1 across five datasets from 0.362 to 0.432 and improving image out-of-distribution detection; these results measure downstream utility, not recovery of the true outlier distribution.

DiffPTS: Rethinking Diffusion ELBO for Probabilistic Time Series Forecasting

DiffPTS jointly trains history-conditioned Gaussian mean and variance estimators with a denoising network, replacing hand-crafted variance supervision with a Gaussian negative log-likelihood derived from the diffusion ELBO and improving CRPS/MSE across nine benchmarks, including a reduction from 0.378/0.637 to 0.270/0.456 against NsDiff on Traffic, without uniformly dominating calibration metrics.

Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models

Kairos handles local time-series complexity and instance-level spectral differences through mixture-of-size encoding and dynamic rotary positional encoding, then predicts multiple future patches in parallel, reaching a normalized MASE of 0.738 on GIFT-Eval with 53M parameters, without uniformly leading in probabilistic or task-level performance.

Multivariate Time Series Forecasting needs Cross Variable Loss

CvLoss leaves the forecasting backbone unchanged and supplements point-wise MSE with constraints on residual differences between forecast patches from different variables, achieving 111 wins, 2 ties, and 1 loss across the paper's 114 backbone-controlled comparison cells, with no loss computation at inference time.

Progressive Memory Transformer: Memory-Aware Attention for Time-Series

PMT exposes writable memory updated along sliding windows as an explicit mid-range representation and separately supervises tokens, memory, and sequence summaries, achieving 84.4% average accuracy across seven low-label classification datasets while a cue-retention probe verifies that memory carries information across windows.

Raw-Routed Mixture of Adapters: A Causal Intervention for Routing Collapse in Time Series Foundation Models

RR-MoA gives a lightweight gate the input before instance normalization while adapter experts retain frozen-backbone hidden states, repairing an input-side information bottleneck and winning all 54 paired comparisons reported against fixed adapters, although the basic version still trails a linear predictor trained from scratch.

Revitalizing Medical Time Series with Vision-Informed Retrieval: A Vision-Language Perspective

ViRe renders the same numerical EEG/ECG segment as a waveform and uses frozen CLIP visual features as a query to retrieve evidence from temporal and channel numerical tokens; the mean six-metric score across six benchmarks rises from Medformer's 77.90 to 82.90, but ViRe does not lead on every dataset, and this is not patient-level diagnostic accuracy.

TimeES: Probabilistic and Deterministic Time Series Forecasting via Evolutionary Spectra

TimeES predicts time-varying complex spectral amplitudes with a lightweight network and synthesizes trajectories using fixed Fourier phases and frequency-level random variables shared across the forecast horizon, unifying deterministic and probabilistic forecasting; its average performance on nine probabilistic benchmarks is strong, but ILI clearly deteriorates, and spectral recovery and uncertainty training require careful interpretation.

TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization

TimeTok encodes time series into discrete token prefixes with coarse-to-fine semantics, adds detail through granularity-block autoregression, and controls output granularity through conditional flow matching, achieving strong conditional refinement and distributional metrics without leading on every downstream predictive metric.

Topological Periodicity Test (TopPT) via Confidence Bound of Time-Delay Embeddings

TopPT turns an early-born, long-lived loop in a delay point cloud into a statistical test with a subsampling confidence radius, providing asymptotic error control over restricted, quantitatively separated signal classes; in synthetic experiments, the raw rule detects both periodic signals without rejecting four structured non-periodic signals, but interpolation-error correction substantially reduces detection, and real respiratory data only assess local loop detection.