📈 Time Series

📷 CVPR2026 · 7 paper notes

📌 Same area in other venues: 🔬 ICLR2026 (121) · 💬 ACL2026 (8) · 🧪 ICML2026 (45) · 🤖 AAAI2026 (31) · 🧠 NeurIPS2025 (54) · 📹 ICCV2025 (4)

🔥 Top topics: Time-Series Forecasting ×3

PFGNet: A Fully Convolutional Frequency-Guided Peripheral Gating Network for Efficient Spatiotemporal Predictive Learning

PFGNet is a pure convolutional spatiotemporal prediction framework that dynamically modulates multi-scale large-kernel peripheral responses via Pixel-level Frequency-guided Gating (PFG) and applies learnable center suppression. Mimicking the center-surround band-pass filtering mechanism of biological vision, it achieves SOTA or near-SOTA performance on Moving MNIST, TaxiBJ, KTH, and Human3.6M benchmarks with minimal parameters and computational cost.

Probabilistic Precipitation Nowcasting with Rectified Flow Transformers

This work proposes FREUD—a framework utilizing a Rectified Flow Transformer as a "compressed first stage." It employs a frame-level encoder to independently encode each frame and a joint video decoder to reconstruct all frames simultaneously, replacing deterministic decoding with probabilistic decoding to quantify uncertainty during the compression stage. Combined with a latent-space rectified flow nowcasting model, it achieves SOTA CRPS (0.0190) and SSIM on the SEVIR precipitation nowcasting benchmark.

Real-Time Long Horizon Air Quality Forecasting via Group-Relative Policy Optimization

This paper addresses long-horizon (48–120 hours) PM concentration forecasting in East Asia. It first releases CMAQ–OBS, a regional dataset aligned with observations, and then employs a two-stage training framework (FAKER-Air) consisting of "SFT with temporal accumulation loss + GRPO with categorical AQI rewards." This aligns the inherent "over-forecasting and high false alarm" issues of MSE training with actual operational costs, reducing the False Alarm Rate (FAR) by 47.3% relative to the SFT baseline while maintaining a competitive F1 score.

SATTC: Structure-Aware Label-Free Test-Time Calibration for Cross-Subject EEG-to-Image Retrieval

SATTC is proposed as a label-free test-time calibration head. By employing a Product-of-Experts (PoE) fusion of a geometric expert (subject-adaptive whitening + adaptive CSLS) and a structural expert (mutual nearest neighbors + bidirectional top-k ranking + category popularity), it operates directly on the similarity matrix of frozen EEG and image encoders. This approach significantly enhances Top-1 accuracy and alleviates the hubness effect in cross-subject EEG-to-image retrieval.

Stable Spike: Dual Consistency Optimization via Bitwise AND Operations for Spiking Neural Networks

Ours proposes the Stable Spike dual consistency optimization framework, which utilizes hardware-friendly bitwise AND operations to decouple stable spike skeletons from multi-timestep spike maps and injects amplitude-aware spike noise to enhance generalization. It improves neuromorphic object recognition accuracy by up to 8.33% under ultra-low latency (\(T=2\)).

STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting

The STCast framework is proposed, which replaces static boundaries with learnable global-regional distributions through Spatial-Aligned Attention (SAA) to adaptively fuse global atmospheric information into regional forecasts. It utilizes Temporal Mixture-of-Experts (TMoE) with monthly dynamic routing to enhance temporal modeling, outperforming existing methods across four tasks: global forecasting, high-resolution regional forecasting, typhoon track prediction, and ensemble forecasting.

Towards Uncertainty-aware Unsupervised Domain Adaptation for Videos and Time-Series with Causal Optimal Transport

This paper proposes Causal-OT, which embeds inter-channel Granger causality graphs into the Optimal Transport (OT) cost matrix for cross-domain alignment. It simultaneously employs entropy-based uncertainty filtering for pseudo-labels to ensure that time-series and video domain adaptation preserves temporal-causal structures without being biased by overconfident pseudo-labels. It achieves an average accuracy improvement of 4.5% across 6 time-series benchmarks and 2.5% across 4 video benchmarks.