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

757 papers, page 15 of 16

701. NeurIPS 2023FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space
702. NeurIPS 2023ForecastPFN: Synthetically-Trained Zero-Shot Forecasting
703. NeurIPS 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
704. NeurIPS 2023Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
705. NeurIPS 2023Fully Dynamic $k$-Clustering in $\tilde O(k)$ Update Time
706. NeurIPS 2023Geometric Neural Diffusion Processes
707. NeurIPS 2023GlucoSynth: Generating Differentially-Private Synthetic Glucose Traces
708. NeurIPS 2023Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians
709. NeurIPS 2023Granger Components Analysis: Unsupervised learning of latent temporal dependencies
710. NeurIPS 2023GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation
711. NeurIPS 2023Improving *day-ahead* Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context
712. NeurIPS 2023Interpreting Unsupervised Anomaly Detection in Security via Rule Extraction
713. NeurIPS 2023Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning
714. NeurIPS 2023Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
715. NeurIPS 2023Large Language Models Are Zero-Shot Time Series Forecasters
716. NeurIPS 2023Latent SDEs on Homogeneous Spaces
717. NeurIPS 2023Linear Time Algorithms for k-means with Multi-Swap Local Search
718. NeurIPS 2023Masked Space-Time Hash Encoding for Efficient Dynamic Scene Reconstruction
719. NeurIPS 2023MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection
720. NeurIPS 2023Most Neural Networks Are Almost Learnable
721. NeurIPS 2023Near-Linear Time Algorithm for the Chamfer Distance
722. NeurIPS 2023Near-Optimal Algorithms for Gaussians with Huber Contamination: Mean Estimation and Linear Regression
723. NeurIPS 2023Neural Lad: A Neural Latent Dynamics Framework for Times Series Modeling
724. NeurIPS 2023Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction
725. NeurIPS 2023Non-adversarial training of Neural SDEs with signature kernel scores
726. NeurIPS 2023On the Constrained Time-Series Generation Problem
727. NeurIPS 2023On the Last-iterate Convergence in Time-varying Zero-sum Games: Extra Gradient Succeeds where Optimism Fails
728. NeurIPS 2023One Fits All: Power General Time Series Analysis by Pretrained LM
729. NeurIPS 2023OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
730. NeurIPS 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection
731. NeurIPS 2023PCF-GAN: generating sequential data via the characteristic function of measures on the path space
732. NeurIPS 2023Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting
733. NeurIPS 2023Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding
734. NeurIPS 2023Regret-Optimal Model-Free Reinforcement Learning for Discounted MDPs with Short Burn-In Time
735. NeurIPS 2023RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing
736. NeurIPS 2023SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise
737. NeurIPS 2023Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels
738. NeurIPS 2023SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
739. NeurIPS 2023SODA: Robust Training of Test-Time Data Adaptors
740. NeurIPS 2023Sparse Graph Learning from Spatiotemporal Time Series
741. NeurIPS 2023Survival Permanental Processes for Survival Analysis with Time-Varying Covariates
742. NeurIPS 2023SutraNets: Sub-series Autoregressive Networks for Long-Sequence, Probabilistic Forecasting
743. NeurIPS 2023Taming Local Effects in Graph-based Spatiotemporal Forecasting
744. NeurIPS 2023Test-Time Amendment with a Coarse Classifier for Fine-Grained Classification
745. NeurIPS 2023The noise level in linear regression with dependent data
746. NeurIPS 2023Time Series as Images: Vision Transformer for Irregularly Sampled Time Series
747. NeurIPS 2023Time Series Kernels based on Nonlinear Vector AutoRegressive Delay Embeddings
748. NeurIPS 2023Time-Independent Information-Theoretic Generalization Bounds for SGLD
749. NeurIPS 2023Time-Reversed Dissipation Induces Duality Between Minimizing Gradient Norm and Function Value
750. NeurIPS 2023Time-uniform confidence bands for the CDF under nonstationarity

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.