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

264 papers, page 2 of 6

51. ICML 2026DAG: A Dual Correlation Network for Time Series Forecasting with Exogenous Variables
52. ICML 2026DecodeShare: Tracing the Shared Subspace of LLM Decode-Time Decisions
53. ICML 2026Decoupling Universal Laws and Environmental Heterogeneity: A Physics-Inspired Framework for Robust Spatio-Temporal Forecasting
54. ICML 2026DeepBlip: Estimating Conditional Average Treatment Effects Over Time
55. ICML 2026Delving into Non-Exchangeability for Conformal Prediction in Graph-Structured Multivariate Time Series
56. ICML 2026Demystifying LLM-as-a-Judge: Analytically Tractable Model for Inference-Time Scaling
57. ICML 2026Denoising without Diffusion: Fixed-Noise Denoiser Anomaly Detection in Tabular Data
58. ICML 2026Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations
59. ICML 2026Discriminative Visual Process Rewards for Scaling Thinking at Test-Time with Images
60. ICML 2026DistMatch: Adaptive Binning via Distribution Matching for Robust Sequential Conformal Prediction
61. ICML 2026Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption
62. ICML 2026DITING: A Weak Degradation Listener for Battery Lifetime Early Prediction
63. ICML 2026Divide and Contrast: Learning Robust Temporal Features without Augmentation
64. ICML 2026DRL-STAF: A Deep Reinforcement Learning Framework for State-Aware Forecasting of Complex Multivariate Hidden Markov Processes
65. ICML 2026DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting
66. ICML 2026DSENet: A Novel Dual-Stream Enhancement Network for Multi-Scale Non-Stationary Time Series Forecasting
67. ICML 2026DualTimesField: Rethinking Time Series as Continuous-Time Trends and Events
68. ICML 2026Dynamic Compression Flows for Neuroscience Data
69. ICML 2026Dynamic Fractal Mamba: A Neural Renormalization Group Flow for Scale-Invariant Sequence Modeling
70. ICML 2026Dynamic Relational Priming Improves Transformer in Multivariate Time Series
71. ICML 2026Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecasting
72. ICML 2026Dywave: Event-Aligned Dynamic Tokenization for Heterogeneous IoT Sensing Signals
73. ICML 2026ECHO: Entropy-Confidence Hybrid Optimization for Test-Time Reinforcement Learning
74. ICML 2026Efficient Learning of Deep State Space Models via Importance Smoothing
75. ICML 2026Efficient Neural Controlled Differential Equations via Attentive Kernel Smoothing
76. ICML 2026Ellipsoidal Time Series Forecasting
77. ICML 2026Empirical Gaussian Processes
78. ICML 2026Escaping the Verifier: Learning to Reason via Demonstrations
79. ICML 2026ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment
80. ICML 2026Evolving Quantitative Reasoning through Self-Play in Digital Twin Markets
81. ICML 2026Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations
82. ICML 2026Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks
83. ICML 2026Extending Prediction-Powered Inference through Conformal Prediction
84. ICML 2026FedPAT: Federated Test-Time Adaptation via Prototype Affinity Topology
85. ICML 2026Finite-time Convergence Analysis of Actor-Critic with Evolving Reward
86. ICML 2026FIPN: Forward Self-Organizing Interpretable Polynomial Networks for Time Series Forecasting
87. ICML 2026Flow Equivariant World Models: Structured Memory for Dynamic Environments
88. ICML 2026FlowState: Sampling-Rate‑Equivariant Time‑Series Forecasting
89. ICML 2026Forward-Chaining Temporal Point Process
90. ICML 2026From geometry to dynamics: Learning overdamped Langevin dynamics from sparse observations with geometric constraints
91. ICML 2026From Observations to States: Latent Time Series Forecasting
92. ICML 2026From Teacher Pathways to Invariant Manifolds: Consensus Subspace Distillation for TSFMs
93. ICML 2026From Text to Forecasts: Bridging Modality Gap with Temporal Evolution Semantic Space
94. ICML 2026Front-Loaded Robust Conformal Prediction: Heavy Calibration, Minimal Test-Time Cost
95. ICML 2026Function-Valued Causal Influence in Nonlinear Time Series
96. ICML 2026FUSE: Ensembling Verifiers with Zero Labeled Data
97. ICML 2026Future-Gain Guided Test-Time Learning for Large Language Models
98. ICML 2026GAAVI: Global Asymptotic Anytime Valid Inference for the Conditional Mean Function
99. ICML 2026Generalizing Multi-Scale Time-Series Modeling with a Single Operator
100. ICML 2026Generative Modeling of Irregular Time Series via SDE-Induced Continuous-Discrete Variational Inference

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.