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

875 papers, page 16 of 18

751. NeurIPS 2026Paloma: Phase-Conditioned Residual Modulation for Time Series Forecasting
752. NeurIPS 2026PAMod: Modeling Cyclical Shifts via Phase-Amplitude Modulation for Non-stationary Time Series Forecasting
753. NeurIPS 2026PhyTS: A Benchmark for Scientific Time Series
754. NeurIPS 2026Polynomial-Time Algorithm for Thiele Voting Rules with Voter Interval Preferences
755. NeurIPS 2026Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals
756. NeurIPS 2026Position: Robust Reasoning Requires Internal Time, Not Scale Alone
757. NeurIPS 2026Precision-Pyramid: Towards real-time neural decoding for fault-tolerant quantum computing
758. NeurIPS 2026Predictive Surprise as Self-Grounding Concept Bottleneck for Interpretable Time Series
759. NeurIPS 2026Prior as Geometry, Not Generator: Weak-Diffusion Test-Time Reconstruction for Dynamic MRI
760. NeurIPS 2026PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation
761. NeurIPS 2026Problem-Dependent Dynamic Regret over a Predictor Class with One-Gradient Feedback
762. NeurIPS 2026ProCTI: Prototype-Refined Global Conditioning for Diffusion-Based Time Series Imputation
763. NeurIPS 2026Progressive Memory Transformer: Memory-Aware Attention for Time-Series
764. NeurIPS 2026Prompt–Activation Duality: Improving Activation Steering via Attention-Level Interventions
765. NeurIPS 2026Proper Agnostic Learning of Functions of Halfspaces
766. NeurIPS 2026Prospective Coding Improves Learning in Deep Continuous-Time Recurrent Networks
767. NeurIPS 2026Provable Test-Time Scaling for Beam Search in LLM Reasoning
768. NeurIPS 2026ProxySearch: Decoupled Inference-Time Scaling for Diffusion Models via Asymmetric Noise-Rank Transfer
769. NeurIPS 2026QWaveNet: Quantum-Enhanced Wavelet Network for Time Series Forecasting
770. NeurIPS 2026Raw-Routed Mixture of Adapters: A Causal Intervention for Routing Collapse in Time Series Foundation Models
771. NeurIPS 2026Reference-Guided Training: Adaptive Gradient Scaling via Per-Sample Loss Comparisons
772. NeurIPS 2026Resolving Time-Frequency Ridge Crossings via Frequency-Rate Lifting
773. NeurIPS 2026Response Time Enhances Alignment with Heterogeneous Preferences
774. NeurIPS 2026Rethinking Expressivity and Efficiency in Test-Time Training
775. NeurIPS 2026Rethinking Post-Training Recipes for Multimodal Time-Series Forecasting
776. NeurIPS 2026Rethinking Time Series Tokenization from a Frequency Perspective
777. NeurIPS 2026Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection
778. NeurIPS 2026RnR: a meta-solver for causal discovery in undersampled time series data
779. NeurIPS 2026Robust Inference-Time Steering of Protein Diffusion Models via Embedding Optimization
780. NeurIPS 2026Robust Residual Correction via Selective Deployment for Time Series Forecasting
781. NeurIPS 2026Robust Stream Classification using Time Neutralising Decision Trees
782. NeurIPS 2026Rubato: Signature Attention for Irregular Multivariate Time Series Forecasting
783. NeurIPS 2026Runtime Analysis of Cartesian Genetic Programming on MAX: A Proven Exponential Speedup
784. NeurIPS 2026S3Former: Sequential, Structural, and Statistical Fusion for Continuous-Time Dynamic Graphs
785. NeurIPS 2026Safe Active Learning with Future Viability Guarantees in Time-Series Models
786. NeurIPS 2026SDFlow: Similarity-Driven Flow Matching for Time Series Generation
787. NeurIPS 2026SDHilb: Schrödinger Dynamics-Guided Neural Network with Adaptive Multi-Scale Hilbert Transform for Time Series Forecasting
788. NeurIPS 2026Self-Trained Verification for Training- and Test-Time Self-Improvement
789. NeurIPS 2026Semantically Complementary Spectral Views Learning for Graph-Level Anomaly Detection
790. NeurIPS 2026SetAD: Semi-Supervised Anomaly Learning in Contextual Sets
791. NeurIPS 2026SIGMA: A Sigmoid-Gated Sampler for Test-Time Scaling in Diffusion Language Models
792. NeurIPS 2026SLiDE: Structured Linear Dynamics for Forecasting with Exogenous Inputs
793. NeurIPS 2026SPECS: Faster Test-Time Scaling through Speculative Drafts and Dynamic Switching
794. NeurIPS 2026Spectral Energy Allocation Enables Source-Free Domain Adaptation in Time Series Forecasting
795. NeurIPS 2026Spectral Transformer Neural Processes
796. NeurIPS 2026Spikes as Detectors: Phase-Conditioned Spiking Dynamics for Time-Series Anomaly Detection
797. NeurIPS 2026SpikeSTAG: A Dendritic Compartmental Spiking Graph Network for Multivariate Time-Series Forecasting
798. NeurIPS 2026Stability-Weighted Direction Regularization Disentangles Generator Shortcuts from Detection Signal
799. NeurIPS 2026Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping
800. NeurIPS 2026Stable Alpha: Adversarial Invariant Representation Learning for Nonlinear Asset Pricing under Temporal Distribution Shifts

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.