{"total":212,"rows":[{"id":"2510.15821","title":"Chronos-2: From Univariate to Universal Forecasting","created":"2025-10-17","cited_by_count":194,"area":null},{"id":"2502.00816","title":"Sundial: A Family of Highly Capable Time Series Foundation Models","created":"2025-02-02","cited_by_count":180,"area":null},{"id":"2505.23719","title":"TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning","created":"2025-05-29","cited_by_count":120,"area":null},{"id":"2511.11698","title":"Moirai 2.0: When Less Is More for Time Series Forecasting","created":"2025-11-12","cited_by_count":69,"area":null},{"id":"2505.14766","title":"This Time is Different: An Observability Perspective on Time Series Foundation Models","created":"2025-05-20","cited_by_count":68,"area":null},{"id":"2509.26468","title":"fev-bench: A Realistic Benchmark for Time Series Forecasting","created":"2025-09-30","cited_by_count":64,"area":null},{"id":"2501.02945","title":"From Tables to Time: Extending TabPFN-v2 to Time Series Forecasting","created":"2025-01-06","cited_by_count":60,"area":null},{"id":"2510.02410","title":"OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data","created":"2025-10-02","cited_by_count":36,"area":"lr"},{"id":"2602.12147","title":"It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks","created":"2026-02-12","cited_by_count":33,"area":null},{"id":"2605.20119","title":"Toto 2.0: Time Series Forecasting Enters the Scaling Era","created":"2026-05-19","cited_by_count":30,"area":null},{"id":"2603.04791","title":"Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling","created":"2026-03-05","cited_by_count":28,"area":"moe"},{"id":"2506.11029","title":"Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model","created":"2025-05-20","cited_by_count":24,"area":"lr"},{"id":"2503.11835","title":"How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlook","created":"2025-03-14","cited_by_count":20,"area":null},{"id":"2606.30410","title":"Beyond IID: How General Are Tabular Foundation Models, Really?","created":"2026-06-29","cited_by_count":20,"area":null},{"id":"2410.16928","title":"xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories","created":"2024-10-22","cited_by_count":19,"area":null},{"id":"2510.25502","title":"TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecasting","created":"2025-10-29","cited_by_count":17,"area":null},{"id":"2510.13654","title":"Rethinking Evaluation in the Era of Time Series Foundation Models: (Un)known Information Leakage Challenges","created":"2025-10-15","cited_by_count":16,"area":"lr"},{"id":"2508.02879","title":"CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data","created":"2025-08-04","cited_by_count":13,"area":"synth"},{"id":"2602.17634","title":"Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting","created":"2026-02-19","cited_by_count":13,"area":null},{"id":"2607.01204","title":"TiRex-2: Generalizing TiRex to Multivariate Data and Streaming","created":"2026-07-01","cited_by_count":13,"area":null},{"id":"2503.01895","title":"Evaluating System 1 vs. 2 Reasoning Approaches for Zero-Shot Time Series Forecasting: A Benchmark and Insights","created":"2025-02-27","cited_by_count":12,"area":"lr"},{"id":"2508.04379","title":"VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Vision Backbones","created":"2025-08-06","cited_by_count":12,"area":null},{"id":"2509.01822","title":"When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference","created":"2025-09-01","cited_by_count":12,"area":"lr"},{"id":"2602.06909","title":"Revisiting the Generic Transformer: Deconstructing a Strong Baseline for Time Series Foundation Models","created":"2026-02-06","cited_by_count":12,"area":null},{"id":"2502.12944","title":"Performance of Zero-Shot Time Series Foundation Models on Cloud Data","created":"2025-02-18","cited_by_count":10,"area":null},{"id":"2509.19975","title":"From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting","created":"2025-09-24","cited_by_count":10,"area":null},{"id":"2603.26017","title":"QuitoBench: A High-Quality Open Time Series Forecasting Benchmark","created":"2026-03-27","cited_by_count":10,"area":null},{"id":"2605.27286","title":"Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling","created":"2026-05-26","cited_by_count":10,"area":null},{"id":"2509.00616","title":"TimeCopilot","created":"2025-08-30","cited_by_count":9,"area":"agents"},{"id":"2509.25826","title":"Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models","created":"2025-09-30","cited_by_count":9,"area":null},{"id":"2510.26777","title":"Pre-trained Forecasting Models: Strong Zero-Shot Feature Extractors for Time Series Classification","created":"2025-10-30","cited_by_count":9,"area":null},{"id":"2602.01776","title":"Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting","created":"2026-02-02","cited_by_count":9,"area":"agents"},{"id":"2606.14222","title":"Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Models","created":"2026-06-12","cited_by_count":9,"area":null},{"id":"2502.14045","title":"There are no Champions in Supervised Long-Term Time Series Forecasting","created":"2025-02-19","cited_by_count":8,"area":null},{"id":"2510.21795","title":"Xihe: Scalable Zero-Shot Time Series Learner Via Hierarchical Interleaved Block Attention","created":"2025-10-20","cited_by_count":8,"area":null},{"id":"2505.15072","title":"MoTime: A Dataset Suite for Multimodal Time Series Forecasting","created":"2025-05-21","cited_by_count":7,"area":null},{"id":"2506.06482","title":"TimeRecipe: A Time-Series Forecasting Recipe via Benchmarking Module Level Effectiveness","created":"2025-06-06","cited_by_count":7,"area":null},{"id":"2506.22039","title":"UniCA: Unified Covariate Adaptation for Time Series Foundation Model","created":"2025-06-27","cited_by_count":7,"area":null},{"id":"2510.19710","title":"SEMPO: Lightweight Foundation Models for Time Series Forecasting","created":"2025-10-22","cited_by_count":7,"area":null},{"id":"2511.05460","title":"Synapse: Adaptive Arbitration of Complementary Expertise in Time Series Foundational Models","created":"2025-11-07","cited_by_count":7,"area":null},{"id":"2602.17149","title":"TimeOmni-VL: Unified Models for Time Series Understanding and Generation","created":"2026-02-19","cited_by_count":7,"area":null},{"id":"2607.06973","title":"Rethinking Multimodal Time-Series Forecasting Evaluation","created":"2026-07-08","cited_by_count":7,"area":null},{"id":"2507.08858","title":"Foundation models for time series forecasting: Application in conformal prediction","created":"2025-07-09","cited_by_count":6,"area":null},{"id":"2510.00742","title":"How Foundational are Foundation Models for Time Series Forecasting?","created":"2025-10-01","cited_by_count":5,"area":null},{"id":"2510.02729","title":"Exploring Accuracy Law for Deep Time Series Forecasters: An Empirical Study","created":"2025-10-03","cited_by_count":5,"area":null},{"id":"2510.06063","title":"TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis","created":"2025-10-07","cited_by_count":5,"area":null},{"id":"2601.00970","title":"Zero-shot Forecasting by Simulation Alone","created":"2026-01-02","cited_by_count":5,"area":null},{"id":"2603.12451","title":"Overcoming the Modality Gap in Context-Aided Forecasting","created":"2026-03-12","cited_by_count":5,"area":null},{"id":"2606.18367","title":"Do Time Series Foundation Model Benchmarks Hide Regime-Dependent Failures? Evidence from Traffic Speed Forecasting","created":"2026-06-16","cited_by_count":5,"area":null},{"id":"2502.03264","title":"GTM: A General Time-series Model for Enhanced Representation Learning of Time-Series Data","created":"2025-02-05","cited_by_count":4,"area":null},{"id":"2510.01111","title":"TsLLM: Augmenting LLMs for General Time Series Understanding and Prediction","created":"2025-10-01","cited_by_count":4,"area":null},{"id":"2602.05287","title":"Position: Universal Time Series Foundation Models Rest on a Category Error","created":"2026-02-05","cited_by_count":4,"area":null},{"id":"2603.08707","title":"Impermanent: A Live Benchmark for Temporal Generalization in Time Series Forecasting","created":"2026-03-09","cited_by_count":4,"area":null},{"id":"2603.14709","title":"Not All Retrievals are Useful: Cross-Attention for Input-Aware RAG in Time Series Forecasting","created":"2026-03-16","cited_by_count":4,"area":"rag"},{"id":"2603.17439","title":"Baguan-TS: A Sequence-Native In-Context Learning Model for Time Series Forecasting with Covariates","created":"2026-03-18","cited_by_count":4,"area":null},{"id":"2604.11529","title":"TempusBench: An Evaluation Framework for Time-Series Forecasting","created":"2026-04-13","cited_by_count":4,"area":null},{"id":"2604.28149","title":"Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models","created":"2026-04-30","cited_by_count":4,"area":null},{"id":"2606.05878","title":"TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning","created":"2026-06-04","cited_by_count":4,"area":null},{"id":"2607.09880","title":"CLIR-Bench: Benchmarking Multimodal Question Answering over Irregular Clinical Time Series","created":"2026-07-10","cited_by_count":4,"area":null},{"id":"2608.13505","title":"Intern-S2-Preview: Scientific Agentic Foundation Model","created":"2026-08-13","cited_by_count":4,"area":"agents"},{"id":"2410.16489","title":"LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling","created":"2024-10-21","cited_by_count":3,"area":null},{"id":"2502.06037","title":"Investigating Compositional Reasoning in Time Series Foundation Models","created":"2025-02-09","cited_by_count":3,"area":"lr"},{"id":"2509.04208","title":"One-Embedding-Fits-All: Efficient Zero-Shot Time Series Forecasting by a Model Zoo","created":"2025-09-04","cited_by_count":3,"area":null},{"id":"2509.13906","title":"TFMAdapter: Lightweight Instance-Level Adaptation of Foundation Models for Forecasting with Covariates","created":"2025-09-17","cited_by_count":3,"area":null},{"id":"2512.20761","title":"TS-Arena -- A Live Forecast Pre-Registration Platform","created":"2025-12-23","cited_by_count":3,"area":null},{"id":"2603.11352","title":"TimeSqueeze: Dynamic Patching for Efficient Time Series Forecasting","created":"2026-03-11","cited_by_count":3,"area":null},{"id":"2603.15506","title":"Seeking SOTA: Time-Series Forecasting Must Adopt Taxonomy-Specific Evaluation to Dispel Illusory Gains","created":"2026-03-16","cited_by_count":3,"area":null},{"id":"2605.00015","title":"TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning","created":"2026-04-18","cited_by_count":3,"area":null},{"id":"2605.08625","title":"Reasoning-Aware Training for Time Series Forecasting","created":"2026-05-09","cited_by_count":3,"area":"lr"},{"id":"2605.22613","title":"Evolutionary Multi-Task Optimization for LLM-Guided Program Discovery","created":"2026-05-21","cited_by_count":3,"area":null},{"id":"2605.26161","title":"TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models","created":"2026-05-24","cited_by_count":3,"area":null},{"id":"2606.01999","title":"Why Do Time Series Models Need Long Context Windows?","created":"2026-06-01","cited_by_count":3,"area":null},{"id":"2607.20002","title":"Post-Training in Time Series Foundation Models: A Unifying Framework","created":"2026-07-22","cited_by_count":3,"area":null},{"id":"2608.13262","title":"Into the ORBIT for Time Series: Training Regimes for Foundation Models","created":"2026-08-13","cited_by_count":3,"area":null},{"id":"2609.04239","title":"EXAONE Finance 1.0: An Attention-free Time Series Foundation Model for Financial Time Series","created":"2026-08-04","cited_by_count":3,"area":null},{"id":"2505.19397","title":"Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains","created":"2025-05-26","cited_by_count":2,"area":null},{"id":"2509.15105","title":"Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting","created":"2025-09-18","cited_by_count":2,"area":"moe"},{"id":"2509.15843","title":"Tsururu: A Python-based Time Series Forecasting Strategies Library","created":"2025-09-19","cited_by_count":2,"area":null},{"id":"2509.19465","title":"A More Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models","created":"2025-09-23","cited_by_count":2,"area":null},{"id":"2509.23695","title":"Estimating Time Series Foundation Model Transferability via In-Context Learning","created":"2025-09-28","cited_by_count":2,"area":null},{"id":"2510.06419","title":"Test-Time Efficient Pretrained Model Portfolios for Time Series Forecasting","created":"2025-10-07","cited_by_count":2,"area":null},{"id":"2510.16060","title":"Beyond Accuracy: Are Time Series Foundation Models Well-Calibrated?","created":"2025-10-17","cited_by_count":2,"area":null},{"id":"2602.14200","title":"TS-Haystack: A Multi-Task Retrieval Benchmark for Long-Context Time-Series Reasoning","created":"2026-02-15","cited_by_count":2,"area":"lr"},{"id":"2603.02840","title":"Adapting Time Series Foundation Models through Data Mixtures","created":"2026-03-03","cited_by_count":2,"area":null},{"id":"2603.15802","title":"Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables","created":"2026-03-16","cited_by_count":2,"area":null},{"id":"2603.22586","title":"A Foundation Model for Instruction-Conditioned In-Context Time Series Tasks","created":"2026-03-23","cited_by_count":2,"area":null},{"id":"2604.06727","title":"Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach","created":"2026-04-08","cited_by_count":2,"area":null},{"id":"2604.18521","title":"IDOBE: Infectious Disease Outbreak forecasting Benchmark Ecosystem","created":"2026-04-20","cited_by_count":2,"area":null},{"id":"2605.12200","title":"Investigating simple target-covariate relationships for Chronos-2 and TabPFN-TS","created":"2026-05-12","cited_by_count":2,"area":null},{"id":"2605.25045","title":"AION: Next-Generation Tasks and Practical Harness for Time Series","created":"2026-05-24","cited_by_count":2,"area":null},{"id":"2606.10798","title":"CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting","created":"2026-06-09","cited_by_count":2,"area":null},{"id":"2606.11625","title":"TimeRouter: Efficient and Adaptive Routing of Time-Series Foundation Models","created":"2026-06-10","cited_by_count":2,"area":"agents"},{"id":"2607.00956","title":"Aionoscope: Debugging Latent-State Accessibility in Time-Series Representations","created":"2026-07-01","cited_by_count":2,"area":null},{"id":"2607.01918","title":"Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis","created":"2026-07-02","cited_by_count":2,"area":null},{"id":"2607.23146","title":"Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting","created":"2026-07-25","cited_by_count":2,"area":null},{"id":"2609.15087","title":"Beyond Numerical Time Series: A Unified Benchmark for Multimodal Forecasting with Heterogeneous Context","created":"2026-09-14","cited_by_count":2,"area":null},{"id":"2609.24444","title":"WPBench: A Comprehensive Benchmark for Wind Power Forecasting","created":"2026-09-21","cited_by_count":2,"area":null},{"id":"2609.24559","title":"$t_0$: A Time-Series Foundation Model for Forecasting with Context","created":"2026-09-21","cited_by_count":2,"area":null},{"id":"2509.06060","title":"ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecasting","created":"2025-09-07","cited_by_count":1,"area":null},{"id":"2509.25263","title":"How Effective Are Time-Series Models for Precipitation Nowcasting? A Comprehensive Benchmark for GNSS-based Precipitation Nowcasting","created":"2025-09-28","cited_by_count":1,"area":null}],"next_cursor":"100"}