Time Series papers, 2023–2026

Key findings

• Time Series went from 272 papers in 2023 to 876 in 2026, roughly 3.2x in three years.
• NeurIPS publishes the most Time Series work: 757 papers from 2023 to 2026, 35% of the four-venue total.
• The fastest-growing overlap is with Multimodal LLMs: papers tagged with both rose from 6 in 2023 to 85 in 2026.
• Among the 5 topics under Graph and Structured Data, it ranks #1 by 2026 paper count; the fastest grower in the group, Molecular Modeling, is up 2.7x versus 3.2x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Time Series papers

298 papers, page 6 of 6

251. NeurIPS 2026The sublevel Flood bifiltration: towards scalable 2-parameter persistent homology
252. NeurIPS 2026TIDES: Implicit Time-Awareness in Selective State Space Models
253. NeurIPS 2026Time series analysis with Gumbel dynamics
254. NeurIPS 2026Time-Frequency Decoupled Cross-Scale Partial Optimal Transport for Time Series Domain Adaptation
255. NeurIPS 2026Time–Frequency Non-Stationary Modeling for Multivariate Time Series Forecasting
256. NeurIPS 2026TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning
257. NeurIPS 2026TimeES: Probabilistic and Deterministic Time Series Forecasting via Evolutionary Spectra
258. NeurIPS 2026TimeOperator: A Function-to-Function Approach to Time Series Modeling
259. NeurIPS 2026TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization
260. NeurIPS 2026Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection
261. NeurIPS 2026To See Far, Look Close: Evolutionary Forecasting for Long-term Time Series
262. NeurIPS 2026TokenCLIP: Token-wise Prompt Learning for Zero-shot Anomaly Detection
263. NeurIPS 2026Tokenization and Architecture Jointly Allocate Component Roles in Time Series Transformers
264. NeurIPS 2026ToLD: Efficient Time Series Forecasting via Tokenized Truncated Latent Diffusion
265. NeurIPS 2026Tool Verification for Test-Time Reinforcement Learning
266. NeurIPS 2026Topological Periodicity Test (TopPT) via Confidence Bound of Time-Delay Embeddings
267. NeurIPS 2026Topology-Aware Optimal Transport for Source-Free Test-Time Adaptation in Anomaly Segmentation
268. NeurIPS 2026Toward Optimal Regret in Robust Pricing: Decoupling Corruption and Time
269. NeurIPS 2026Towards Generalist Graph-Level Anomaly Detection
270. NeurIPS 2026Towards Principled Efficient Rollout Allocation in Test-Time Training
271. NeurIPS 2026Training Agent to Scale Inference-Time Reasoning
272. NeurIPS 2026Training Quality Determines Efficiency Boundaries in Test-Time Reasoning
273. NeurIPS 2026Training-Free Cultural Alignment of Large Language Models via Persona Disagreement
274. NeurIPS 2026Transferable, Time-Parallel Graph Dynamics via Space-Time Factorization
275. NeurIPS 2026TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond
276. NeurIPS 2026TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning
277. NeurIPS 2026TSB-SEG: A Systematic Time-Series Segmentation Benchmark
278. NeurIPS 2026TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning
279. NeurIPS 2026TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting
280. NeurIPS 2026TTB: Test-time MLP Baking for Efficient Rendering of Decoder-only View Synthesis Models
281. NeurIPS 2026Uniform-in-Time Weak Propagation of Chaos in Shallow Neural Networks
282. NeurIPS 2026Universal Approximation Theorems for Dynamical Systems with Infinite-Time Horizon Guarantees
283. NeurIPS 2026Universal Time Series Generation with Neural Controlled Differential Equations
284. NeurIPS 2026Unsupervised Physics Informed Decomposition of Incomplete Time-Resolved Spectroscopy
285. NeurIPS 2026Velocity Ambiguity Profiles: Time-Resolved Bayes-Risk Diagnostics for Flow Matching
286. NeurIPS 2026VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection
287. NeurIPS 2026VGB for Masked Diffusion Model: Efficient Test-time Scaling for Reward Satisfaction and Sample Editing
288. NeurIPS 2026Volatility-Whitened Probabilistic Residual Modeling for Long-Term Time Series Forecasting
289. NeurIPS 2026WebSpline: Structure-Informed Splines for Real-Time 3D Gaussians from Monocular Videos
290. NeurIPS 2026Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs
291. NeurIPS 2026What Does an Observability Forecasting Foundation Model Know?
292. NeurIPS 2026What should post-training optimize? A test-time scaling law perspective
293. NeurIPS 2026What Time Is It? How Data Geometry Makes Time Conditioning Optional for Flow Matching
294. NeurIPS 2026When Does Sequential Detection Collapse to a Scalar? A Necessary and Sufficient Characterisation
295. NeurIPS 2026When to Trust Memory: Retrieval-Guided Probabilistic Spatiotemporal Forecasting under Distribution Shift
296. NeurIPS 2026Why Do Time Series Models Need Long Context Windows?
297. NeurIPS 2026WILD: Widely Linear Conditioning for Time Series Forecasting
298. NeurIPS 2026ZNO: Stable Rational Neural Operators in the Z-Domain for Discrete-Time Dynamics

FAQ

How many Time Series papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

2,157 in total: 272 in 2023, 424 in 2024, 585 in 2025, 876 in 2026.

How many Time Series papers are at NeurIPS 2026?

NeurIPS 2026 has 298 Time Series papers, versus 221 the year before.

How many Time Series papers are at ICLR 2026?

ICLR 2026 has 220 Time Series papers, versus 131 the year before.

How many Time Series papers are at ICML 2026?

ICML 2026 has 264 Time Series papers, versus 159 the year before.

How many Time Series papers are at CVPR 2026?

CVPR 2026 has 94 Time Series papers, versus 74 the year before.

Which conference publishes the most Time Series papers?

NeurIPS, with 757 papers from 2023 to 2026.