📈 Time Series¶

📷 CVPR2026 · 8 paper notes

A Frame is Worth One Token: Efficient Generative World Modeling with Delta Tokens: This paper proposes DeltaTok, which compresses inter-frame VFM feature differences into a single delta token. Combined with Best-of-Many training, DeltaWorld efficiently generates diverse future predictions in a single forward pass. The model uses only 1/35 the parameters of Cosmos and 1/2000 the FLOPs, yet achieves superior performance on dense prediction tasks.
Competition-Aware CPC Forecasting with Near-Market Coverage: This paper reformulates CPC forecasting in search advertising as a time series prediction problem under partially observable competition states. Three observable proxies — semantic similarity, CPC trajectory alignment, and geographic intent — are constructed to approximate latent competition, and are subsequently injected into forecasters as covariates and graph priors respectively. The proposed framework achieves substantial improvements over purely autoregressive baselines on medium- and long-term forecasting horizons.
Competition-Aware CPC Forecasting with Near-Market Coverage: This paper reframes cost-per-click (CPC) forecasting in paid search advertising as a partial competition observability problem. By constructing three families of competition proxy signals — semantic neighborhood, DTW behavioral neighborhood, and geographic intent — and integrating them with temporal foundation models (Chronos-2/TimeGPT/Moirai) and spatiotemporal GNNs, the proposed framework achieves significant improvements in medium-to-long-term forecasting accuracy over 1,811 keyword time series.
L2GTX: From Local to Global Time Series Explanations: L2GTX is proposed as a fully model-agnostic local-to-global explanation method for time series, employing parameterized event primitives (increasing/decreasing trends, local extrema) as explanation units. Through hierarchical clustering merging, greedy budget selection, and attribute statistics aggregation, it produces compact and faithful class-level global explanations across 6 UCR datasets (GF = 0.792 on ECG200 with FCN).
L2GTX: From Local to Global Time Series Explanations: L2GTX proposes a fully model-agnostic local-to-global explanation framework for time series classification. It extracts parameterized temporal event primitives (PEPs)—trends and extrema—from LOMATCE local explanations, merges redundant clusters across instances via hierarchical clustering, selects representative instances through submodular optimization, and aggregates these into concise class-level global explanations. The method maintains stable global faithfulness across six time series classification datasets.
PFGNet: A Fully Convolutional Frequency-Guided Peripheral Gating Network for Efficient Spatiotemporal Predictive Learning: This paper proposes PFGNet, a fully convolutional spatiotemporal prediction framework that dynamically modulates multi-scale large-kernel peripheral responses via pixel-wise frequency-guided gating (PFG) while applying learnable center inhibition, thereby simulating the biological center-surround bandpass filtering mechanism of the visual system. PFGNet achieves state-of-the-art or near state-of-the-art performance on four benchmarks—Moving MNIST, TaxiBJ, KTH, and Human3.6M—with remarkably few parameters and low computational cost.
Stable Spike: Dual Consistency Optimization via Bitwise AND Operations for Spiking Neural Networks: This paper proposes Stable Spike, a dual consistency optimization framework that employs the hardware-friendly bitwise AND operation to decouple a stable spike skeleton \(\tilde{S}\) from multi-timestep spike maps, and injects amplitude-aware spike noise to enhance generalization. The method achieves up to 8.33% accuracy improvement on neuromorphic object recognition tasks under ultra-low latency (\(T=2\)).
STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting: This paper proposes STCast, a framework that replaces static boundary cropping with learnable global-regional distributions via Spatial-Aligned Attention (SAA) to adaptively integrate global atmospheric information into regional forecasting, and employs Temporal Mixture-of-Experts (TMoE) with month-conditioned dynamic routing to enhance temporal modeling. STCast achieves state-of-the-art performance across four tasks: global forecasting, high-resolution regional forecasting, typhoon track prediction, and ensemble forecasting.