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

585 papers, page 11 of 12

501. NeurIPS 2025PhysDiff: A Physically-Guided Diffusion Model for Multivariate Time Series Anomaly Detection
502. NeurIPS 2025Physics-informed Reduced Order Modeling of Time-dependent PDEs via Differentiable Solvers
503. NeurIPS 2025PIPE: Physics-Informed Position Encoding for Alignment of Satellite Images and Time Series in Typhoon Forecasting
504. NeurIPS 2025PMLF: A Physics-Guided Multiscale Loss Framework for Structurally Heterogeneous Time Series
505. NeurIPS 2025POCO: Scalable Neural Forecasting through Population Conditioning
506. NeurIPS 2025PointMAC: Meta-Learned Adaptation for Robust Test-Time Point Cloud Completion
507. NeurIPS 2025Pool Me Wisely: On the Effect of Pooling in Transformer-Based Models
508. NeurIPS 2025Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities
509. NeurIPS 2025Provable Scaling Laws for the Test-Time Compute of Large Language Models
510. NeurIPS 2025Quantifying Statistical Significance of Deep Nearest Neighbor Anomaly Detection via Selective Inference
511. NeurIPS 2025Real-Time Scene-Adaptive Tone Mapping for High-Dynamic Range Object Detection
512. NeurIPS 2025Reconstruct, Inpaint, Test-Time Finetune: Dynamic Novel-view Synthesis from Monocular Videos
513. NeurIPS 2025Redundancy-Aware Test-Time Graph Out-of-Distribution Detection
514. NeurIPS 2025Remasking Discrete Diffusion Models with Inference-Time Scaling
515. NeurIPS 2025Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning
516. NeurIPS 2025Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling
517. NeurIPS 2025Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods
518. NeurIPS 2025Rotary Masked Autoencoders are Versatile Learners
519. NeurIPS 2025Sampling-Efficient Test-Time Scaling: Self-Estimating the Best-of-N Sampling in Early Decoding
520. NeurIPS 2025Scalable Signature Kernel Computations via Local Neumann Series Expansions
521. NeurIPS 2025Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
522. NeurIPS 2025Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
523. NeurIPS 2025ScatterAD: Temporal-Topological Scattering Mechanism for Time Series Anomaly Detection
524. NeurIPS 2025Searching Latent Program Spaces
525. NeurIPS 2025Selective Learning for Deep Time Series Forecasting
526. NeurIPS 2025Self-Perturbed Anomaly-Aware Graph Dynamics for Multivariate Time-Series Anomaly Detection
527. NeurIPS 2025Semi-supervised Graph Anomaly Detection via Robust Homophily Learning
528. NeurIPS 2025SEMPO: Lightweight Foundation Models for Time Series Forecasting
529. NeurIPS 2025Sequence Modeling with Spectral Mean Flows
530. NeurIPS 2025SGN: Shifted Window-Based Hierarchical Variable Grouping for Multivariate Time Series Classification
531. NeurIPS 2025ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models
532. NeurIPS 2025Single-Step Operator Learning for Conditioned Time-Series Diffusion Models
533. NeurIPS 2025Sinusoidal Initialization, Time for a New Start
534. NeurIPS 2025SPACE: SPike-Aware Consistency Enhancement for Test-Time Adaptation in Spiking Neural Networks
535. NeurIPS 2025SpecReason: Fast and Accurate Inference-Time Compute via Speculative Reasoning
536. NeurIPS 2025STaRFormer: Semi-Supervised Task-Informed Representation Learning via Dynamic Attention-Based Regional Masking for Sequential Data
537. NeurIPS 2025Structured Linear CDEs: Maximally Expressive and Parallel-in-Time Sequence Models
538. NeurIPS 2025Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models
539. NeurIPS 2025Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection
540. NeurIPS 2025Synthetic Series-Symbol Data Generation for Time Series Foundation Models
541. NeurIPS 2025SynTSBench: Rethinking Temporal Pattern Learning in Deep Learning Models for Time Series
542. NeurIPS 2025TARFVAE: Efficient One-Step Generative Time Series Forecasting via TARFLOW based VAE
543. NeurIPS 2025Test Time Scaling for Neural Processes
544. NeurIPS 2025Test-Time Adaptation by Causal Trimming
545. NeurIPS 2025Test3R: Learning to Reconstruct 3D at Test Time
546. NeurIPS 2025The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
547. NeurIPS 2025The Dual Nature of Plasticity Loss in Deep Continual Learning: Dissection and Mitigation
548. NeurIPS 2025The Emergence of Abstract Thought in Large Language Models Beyond Any Language
549. NeurIPS 2025This Time is Different: An Observability Perspective on Time Series Foundation Models
550. NeurIPS 2025TIDMAD: Time Series Dataset for Discovering Dark Matter with AI Denoising

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.