时间序列论文趋势 2023–2026
预测与异常检测
统计范围:NeurIPS、ICLR、ICML、CVPR 四个会议,2023–2026 年,共 2,157 篇被归入该主题的论文。
主要结论
- 时间序列论文从 2023 年的 272 篇增长到 2026 年的 876 篇,三年间扩大到约 3.2 倍。
- NeurIPS 是发时间序列论文最多的会议,2023–2026 年共 757 篇,占四个会议总量的 35%。
- 交叉方向里涨得最快的是多模态大模型:同时属于两个主题的论文从 2023 年的 6 篇增至 2026 年的 85 篇。
- 在「图与结构化数据」的 5 个主题里,它的 2026 年论文数排第 1;同组增长最快的分子建模是 2.7 倍,时间序列是 3.2 倍。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 91 | 60 | 62 | 59 | 272 |
| 2024 | 147 | 95 | 114 | 68 | 424 |
| 2025 | 221 | 131 | 159 | 74 | 585 |
| 2026 | 298 | 220 | 264 | 94 | 876 |
代表论文
按年份和会议均匀挑选,方便快速看到每一届的样貌
- NeurIPS 2026A Batched Hartigan's $k$-Means Clustering
- ICML 2026A Control-Theoretic View of Mamba on Stability and Robustness
- ICLR 2026A Single Architecture for Representing Invariance Under Any Space Group
- CVPR 2026Activation Matters Test-time Activated Negative Labels for OOD Detection with
- NeurIPS 2025Abstain Mask Retain Core: Time Series Prediction by Adaptive Masking Loss with Representation Consistency
- ICML 2025$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
- ICLR 2025A deep inverse-mapping model for a flapping robotic wing
- CVPR 2025Beyond Single-Modal Boundary: Cross-Modal Anomaly Detection through Visual Prototype and Harmonization
- NeurIPS 2024A Continuous-time Stochastic Gradient Descent Method for Continuous Data
- ICML 2024$S^2$IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting
- ICLR 2024ARM: Refining Multivariate Forecasting with Adaptive Temporal-Contextual Learning
- CVPR 2024Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation
- NeurIPS 2023A Sublinear-Time Spectral Clustering Oracle with Improved Preprocessing Time
- ICML 2023Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time
- ICLR 2023A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
- CVPR 2023ActMAD: Activation Matching To Align Distributions for Test-Time-Training
- NeurIPS 2026A Locally Tokenized Generative Model for Robust Time-Series Watermarking
- ICML 2026ADHD Disease Detection Based on Short- and Long-Term Brain Function Encoding and Memory Graph Network
- ICLR 2026A Spectral-Grassmann Wasserstein metric for operator representations of dynamical systems
- CVPR 2026Continuous Exposure-Time Modeling for Realistic Atmospheric Turbulence Synthesis
全部时间序列论文
共 298 篇,第 2 / 6 页
- NeurIPS 2026Coordinating Hundreds of RL Agents through Scalable Inference-Time Search
- NeurIPS 2026Counterfactual Explanations for Time-Series Classification via Constrained Flow Matching
- NeurIPS 2026Crosscoding Through Time: Sparse Feature Discovery Across Sequence Positions
- NeurIPS 2026CVTA: Cross-Variable Temporal Attention for Multivariate Irregular Time Series Prediction
- NeurIPS 2026Cylindrical Geodesic Flow Matching for Quasiperiodic Physiological Signal Transformation
- NeurIPS 2026Decomposing Temporal and Job-Induced Dynamics for Probabilistic Computing Workload Forecasting via Graph-Conditioned Dual-Branch Diffusion
- NeurIPS 2026DEDCA: Test-Time Adaptation for Generalized AI-Generated Image Detection
- NeurIPS 2026Deployment-Memory LLM Test-Time Training Should Require Behavioral Evidence Beyond Perplexity
- NeurIPS 2026Deployment-Time Online Imitation Learning from Corrective Demonstrations
- NeurIPS 2026DetectViT: Test-time Backdoor Detection for Vision Transformers via Inter-Head Attention Discrepancy
- NeurIPS 2026Differencing the Diffusion Trajectory toward Uncertain Components for Time Series Forecasting
- NeurIPS 2026DiffPTS: Rethinking Diffusion ELBO for Probabilistic Time Series Forecasting
- NeurIPS 2026Diffusion-Time Concept Manifolds: Sparse Autoencoder Groups for Interpreting Denoising Language Models
- NeurIPS 2026Disentangling Continuous-Time Latent Dynamics: Identifiability of Latent SDEs via Diffusion Shifts
- NeurIPS 2026Distillation of Foundation Models for Time-dependent PDEs
- NeurIPS 2026Distilling Multi-Teacher Scoring Principles for Unsupervised Time Series Anomaly Detection
- NeurIPS 2026Do Deep Networks Forget Initialization? A Forgetting-Time View of Practical Inductive Bias
- NeurIPS 2026DoFP-Aligned Lookup Tables for Real-Time Polarization Demosaicking
- NeurIPS 2026DSR-TSF: Spectrum-Driven Dynamic Routing for Efficient Long-Horizon Time Series Forecasting
- NeurIPS 2026DSSNet: Deep Spectral Structure Profiling Network for Traffic Flow Prediction
- NeurIPS 2026Dynamic Dual-Feedback Conformal Inference for Time Series Forecasting
- NeurIPS 2026Dynamic Tokenization via Reinforcement Patching: End-to-end Training and Zero-shot Transfer
- NeurIPS 2026DynaPFN: Zero-Shot Dynamical System Forecasting with Tabular Prior-Fitted Networks
- NeurIPS 2026Early Signals, Strong Decisions: Prefix-Guided Sampling for Parallel Test-Time Scaling
- NeurIPS 2026Echoes of Error: Residual-Directional Local Rollout Consistency for Timeseries Forecasting
- NeurIPS 2026Empowering Time Series Analysis with Large-Scale Multimodal Pretraining
- NeurIPS 2026Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach
- NeurIPS 2026Entropy Guided Dynamic Patch Segmentation for Time Series Transformers
- NeurIPS 2026ESENSC: A Polynomial-Time Axiomatic Alternative to SHAP
- NeurIPS 2026EVA-0: Test-Time Model Evolution with Only Two Forward Passes per Sample
- NeurIPS 2026Evaluating Deployable Inference-Time Error Prediction in Vision MoEs
- NeurIPS 2026Evaluating Uncertainty Calibration in Probabilistic Time Series Foundation Models
- NeurIPS 2026EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales
- NeurIPS 2026Exact Flow Linear Attention: Exact Solution from Continuous-Time Dynamics
- NeurIPS 2026ExComm: Exploration-Stage Communication for Error-Resilient Agentic Test-Time Scaling
- NeurIPS 2026FashionChameleon: Towards Real-Time and Interactive Human-Garment Video Customization
- NeurIPS 2026Fast-WAM: Do World Action Models Need Test-time Future Imagination?
- NeurIPS 2026FASTER: Rethinking Real-Time Flow VLAs
- NeurIPS 2026FENet: Functional Embedding Neural Network for Change-Point Detection in Functional Time Series
- NeurIPS 2026fev-bench: A Realistic Benchmark for Time Series Forecasting
- NeurIPS 2026Filter Banks: from Low-Rank Representations to Deep Models for Efficient Time Series Forecasting
- NeurIPS 2026FIND: Frequency Invariance Disentanglement for Test-Time Adaptation in LiDAR 3D Detection
- NeurIPS 2026Finite Time Analysis of Risk-Sensitive RL via Noisy Power Iteration
- NeurIPS 2026First-Token Attraction in Mamba Dynamics
- NeurIPS 2026FISC: Time-Series Forecasting via First-Layer Statistical Calibration Constraints
- NeurIPS 2026FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting
- NeurIPS 2026Flow Matching from Viewpoint of Proximal Operators
- NeurIPS 2026Flow Mismatching: Unsupervised Anomaly Detection via Velocity Discrepancies in Flow Matching Models
- NeurIPS 2026Flow+Diff: Unifying Flow and Diffusion Representations for Multivariate Time Series Anomaly Detection
- NeurIPS 2026Focus, Align, and Diffuse: Time-Series-Aware Keyframe Diffusion for Cardiac Dynamic Synthesis from Sparse Observations
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后,写综述、挑方向或准备投稿时,不用离开对话框就能拿到这页的数字:每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文,AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查时间序列在 NeurIPS 2023–2026 的论文趋势,哪一年涨得最快?
AI 会调用的工具:get_topic_stats {"topic": "time-series"}
常见问题
- 2023–2026 年四大会议一共有多少篇时间序列论文?
- 共 2,157 篇,按年份分别是 2023 年 272 篇、2024 年 424 篇、2025 年 585 篇、2026 年 876 篇。
- NeurIPS 2026 有多少篇时间序列论文?
- NeurIPS 2026 有 298 篇时间序列论文,上一届是 221 篇。
- ICLR 2026 有多少篇时间序列论文?
- ICLR 2026 有 220 篇时间序列论文,上一届是 131 篇。
- ICML 2026 有多少篇时间序列论文?
- ICML 2026 有 264 篇时间序列论文,上一届是 159 篇。
- CVPR 2026 有多少篇时间序列论文?
- CVPR 2026 有 94 篇时间序列论文,上一届是 74 篇。
- 哪个会议的时间序列论文最多?
- NeurIPS,2023–2026 年共 757 篇。