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

221 papers, page 4 of 5

151. NeurIPS 2025Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning
152. NeurIPS 2025Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling
153. NeurIPS 2025Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods
154. NeurIPS 2025Rotary Masked Autoencoders are Versatile Learners
155. NeurIPS 2025Sampling-Efficient Test-Time Scaling: Self-Estimating the Best-of-N Sampling in Early Decoding
156. NeurIPS 2025Scalable Signature Kernel Computations via Local Neumann Series Expansions
157. NeurIPS 2025Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
158. NeurIPS 2025Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
159. NeurIPS 2025ScatterAD: Temporal-Topological Scattering Mechanism for Time Series Anomaly Detection
160. NeurIPS 2025Searching Latent Program Spaces
161. NeurIPS 2025Selective Learning for Deep Time Series Forecasting
162. NeurIPS 2025Self-Perturbed Anomaly-Aware Graph Dynamics for Multivariate Time-Series Anomaly Detection
163. NeurIPS 2025Semi-supervised Graph Anomaly Detection via Robust Homophily Learning
164. NeurIPS 2025SEMPO: Lightweight Foundation Models for Time Series Forecasting
165. NeurIPS 2025Sequence Modeling with Spectral Mean Flows
166. NeurIPS 2025SGN: Shifted Window-Based Hierarchical Variable Grouping for Multivariate Time Series Classification
167. NeurIPS 2025ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models
168. NeurIPS 2025Single-Step Operator Learning for Conditioned Time-Series Diffusion Models
169. NeurIPS 2025Sinusoidal Initialization, Time for a New Start
170. NeurIPS 2025SPACE: SPike-Aware Consistency Enhancement for Test-Time Adaptation in Spiking Neural Networks
171. NeurIPS 2025SpecReason: Fast and Accurate Inference-Time Compute via Speculative Reasoning
172. NeurIPS 2025STaRFormer: Semi-Supervised Task-Informed Representation Learning via Dynamic Attention-Based Regional Masking for Sequential Data
173. NeurIPS 2025Structured Linear CDEs: Maximally Expressive and Parallel-in-Time Sequence Models
174. NeurIPS 2025Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models
175. NeurIPS 2025Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection
176. NeurIPS 2025Synthetic Series-Symbol Data Generation for Time Series Foundation Models
177. NeurIPS 2025SynTSBench: Rethinking Temporal Pattern Learning in Deep Learning Models for Time Series
178. NeurIPS 2025TARFVAE: Efficient One-Step Generative Time Series Forecasting via TARFLOW based VAE
179. NeurIPS 2025Test Time Scaling for Neural Processes
180. NeurIPS 2025Test-Time Adaptation by Causal Trimming
181. NeurIPS 2025Test3R: Learning to Reconstruct 3D at Test Time
182. NeurIPS 2025The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
183. NeurIPS 2025The Dual Nature of Plasticity Loss in Deep Continual Learning: Dissection and Mitigation
184. NeurIPS 2025The Emergence of Abstract Thought in Large Language Models Beyond Any Language
185. NeurIPS 2025This Time is Different: An Observability Perspective on Time Series Foundation Models
186. NeurIPS 2025TIDMAD: Time Series Dataset for Discovering Dark Matter with AI Denoising
187. NeurIPS 2025Time Reversal Symmetry for Efficient Robotic Manipulations in Deep Reinforcement Learning
188. NeurIPS 2025Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach
189. NeurIPS 2025Time-Embedded Algorithm Unrolling for Computational MRI
190. NeurIPS 2025Time-Evolving Dynamical System for Learning Latent Representations of Mouse Visual Neural Activity
191. NeurIPS 2025Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series
192. NeurIPS 2025Time-o1: Time-Series Forecasting Needs Transformed Label Alignment
193. NeurIPS 2025TimeEmb: A Lightweight Static-Dynamic Disentanglement Framework for Time Series Forecasting
194. NeurIPS 2025TimePerceiver: An Encoder-Decoder Framework for Generalized Time-Series Forecasting
195. NeurIPS 2025TimeWak: Temporal Chained-Hashing Watermark for Time Series Data
196. NeurIPS 2025TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop
197. NeurIPS 2025TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning
198. NeurIPS 2025Titans: Learning to Memorize at Test Time
199. NeurIPS 2025ToF-IP: Time-of-Flight Enhanced Sparse Inertial Poser for Real-time Human Motion Capture
200. NeurIPS 2025Toward Interpretable Evaluation Measures for Time Series Segmentation

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.