📈 Time Series

📷 CVPR2025 · 5 paper notes

📌 Same area in other venues: 📷 CVPR2026 (7) · 🔬 ICLR2026 (121) · 💬 ACL2026 (8) · 🧪 ICML2026 (45) · 🤖 AAAI2026 (31) · 🧠 NeurIPS2025 (54)

🔥 Top topics: Time-Series Forecasting ×2

Competition-Aware CPC Forecasting with Near-Market Coverage

This work reformulates paid search CPC forecasting as a "forecasting under partially observable competition" problem. It approximates unobservable competitive states using three types of competition proxies: semantic neighborhoods (via Transformer embeddings), behavioral neighborhoods (via DTW alignment), and geographic intent. Evaluations on Google Ads data covering 1,811 keywords over 127 weeks demonstrate that competition-aware enhancements significantly outperform univariate and weak-context baselines in medium- to long-term forecasting (6/12 weeks).

DejaVid: Encoder-Agnostic Learned Temporal Matching for Video Classification

This paper proposes DejaVid, an encoder-agnostic, lightweight approach for enhancing video classification. Instead of representing a video with a single embedding, DejaVid represents it as a variable-length Temporal Sequence of Embeddings (TSE). By learning importance weights for each time step and feature dimension, combined with an improved differentiable DTW algorithm for temporal alignment classification, it achieves SOTA results of 77.2% on SSV2 and 89.1% on K400 with an increase of only <1.8% parameters.

FLAVC: Learned Video Compression with Feature Level Attention

This work proposes FLAVC, which introduces a Feature-level Attention (FLA) module into the learned video compression (LVC) framework. By converting high-level local patch embeddings into one-dimensional batch-wise vectors and replacing traditional attention weights with a global context matrix, FLA achieves full-frame-level global perception. Combined with a Dense Overlapping Patcher and a hybrid Transformer-CNN encoder, FLAVC achieves state-of-the-art rate-distortion performance across four video compression datasets.

L2GTX: From Local to Global Time Series Explanations

L2GTX proposes a completely model-agnostic global explanation method for time series classification. By aggregating Parameterized Event Primitives (PEPs) generated by LOMATCE, it constructs class-level global explanations, maintaining stable global fidelity (\(R^2\)) across six benchmark datasets.

Learning Extremely High Density Crowds as Active Matters

This paper models extremely high-density crowds (\(\ge 5 \text{ people/m}^2\)) as active matter, proposing a neural stochastic differential equation system that combines a novel "crowd material" stress model with Toner-Tu active forces. The system learns and predicts crowd dynamics directly from in-the-wild video optical flow using a hybrid Eulerian-Lagrangian CrowdMPM framework.