mlsanityData to 7 Oct 2026

From Tables to Time: Extending TabPFN-v2 to Time Series Forecasting

arXiv 2501.02945 ↗First posted 6 Jan 2025cs.LGcited by 49

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Abstract (arXiv)

Recent progress in foundation models has enabled strong zero-shot performance for time series forecasting. In this work, we show that such capabilities can also emerge from tabular foundation models. We introduce TabPFN-TS, a simple method that treats forecasting as a tabular regression problem by combining lightweight temporal featurization with the pretrained TabPFN-v2. This formulation requires no time-series-specific pretraining and naturally supports both univariate and covariate-informed forecasting. Despite its compact size (11M parameters), TabPFN-TS achieves state-of-the-art performance on covariate-informed forecasting and competitive accuracy on univariate forecasting across the GIFT-Eval and fev-bench benchmarks. We further provide controlled analyses examining how the model interprets temporal structure, how featurization choices affect accuracy, and how forecasts change under alternative tabular backbones. Together, our results demonstrate that tabular foundation models--when paired with suitable temporal features--offer an efficient and versatile alternative for forecasting, bridging tabular and time-series learning within a unified framework. Code is available at https://github.com/PriorLabs/tabpfn-time-series.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). Builds on 10 of its 21 references that are in the map. First posted on arXiv 6 Jan 2025.

Builds on

10 of 21 references.

References · 10

  1. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  2. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 748
  3. 2024Chronos: Learning the Language of Time Seriescited by 712
  4. 2023A decoder-only foundation model for time-series forecastingcited by 650
  5. 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Secondcited by 643
  6. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
  7. 2024GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluationcited by 188
  8. 2024TabDPT: Scaling Tabular Foundation Models on Real Datacited by 97
  9. 2024Toto: Time Series Optimized Transformer for Observabilitycited by 44
  10. 2023AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecastingcited by 36

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 19; 2026: 30.

49 papers.

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  1. 2025Chronos-2: From Univariate to Universal Forecastingcited by 158
  2. 2025TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problemscited by 33
  3. 2025Turning Tabular Foundation Models into Graph Foundation Modelscited by 30
  4. 2025Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Modelcited by 21
  5. 2025Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Modelscited by 15
  6. 2025Bringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Modelscited by 12
  7. 2025Evaluating System 1 vs. 2 Reasoning Approaches for Zero-Shot Time Series Forecasting: A Benchmark and Insightscited by 11
  8. 2025TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Modelscited by 10
  9. 2025TimeCopilotcited by 8
  10. 2025Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?cited by 8
  11. 2026Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modelingcited by 7
  12. 2025CoRA: Covariate-Aware Adaptation of Time Series Foundation Modelscited by 5
37 more
  1. 2025Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Modelscited by 4
  2. 2026TempusBench: An Evaluation Framework for Time-Series Forecastingcited by 4
  3. 2026RocketPFN: Accurate Time Series Classification via In-Context Learningcited by 4
  4. 2025Realistic Evaluation of TabPFN v2 in Open Environmentscited by 3
  5. 2025One-Embedding-Fits-All: Efficient Zero-Shot Time Series Forecasting by a Model Zoocited by 3
  6. 2025TiCT: A Synthetically Pre-Trained Foundation Model for Time Series Classificationcited by 3
  7. 2026Reasoning-Aware Training for Time Series Forecastingcited by 3
  8. 2026Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networkscited by 3
  9. 2026Into the ORBIT for Time Series: Training Regimes for Foundation Modelscited by 3
  10. 2025Are Time-Indexed Foundation Models the Future of Time Series Imputation?cited by 1
  11. 2025From Tables to Signals: Revealing Spectral Adaptivity in TabPFNcited by 1
  12. 2026In-Context Learning Under Regime Changecited by 1
  13. 2026FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Modelscited by 1
  14. 2026RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Modelscited by 1
  15. 2026Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignmentcited by 1
  16. 2026Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecastingcited by 1
  17. 2026Memory Efficient Tabular Foundation Modelscited by 1
  18. 2026Forecast Collapse in Time-Series Foundation Modelscited by 1
  19. 2026TinyCast: Probabilistic Zero-Shot Forecasting with Computed Periodicitycited by 1
  20. 2026Foundation models for electricity price forecasting and battery arbitrage: Can they replace market-specific forecasting models?cited by 1
  21. 2025Foundation Model Forecasts: Form and Functioncited by 0
  22. 2025Tiny-TSM: Efficiently Training a Lightweight SOTA Time Series Foundation Modelcited by 0
  23. 2026PRIM: Meta-Learned Bayesian Root Cause Analysiscited by 0
  24. 2026Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecastingcited by 0
  25. 2026In-Context Multiple Instance Learningcited by 0
  26. 2026Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecastingcited by 0
  27. 2026The Simulacrum: Decision-Theoretic Pretraining for Near-Optimal Time-Series Forecasting and Inferencecited by 0
  28. 2026Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk, Distributional Shifts, and Covariate Dependencecited by 0
  29. 2026Learning to Forecast Crop Growth from Earth Observation Datacited by 0
  30. 2026Systematic Evaluation of TabPFN-TS and Chronos-2 for Zero-Shot Heat Load Forecasting in District Heating Networkscited by 0
  31. 2026TSPFN: A Temporal Tabular Foundation Model for Physiological Time Series Classificationcited by 0
  32. 2026t0: A Time-Series Foundation Model for Forecasting with Contextcited by 0
  33. 2026EXAONE Demand 1.0: A Time Series Foundation Model for Demand Forecastingcited by 0
  34. 2026Medium-Term Multi-Resolution Electric Load Forecasting using Economic Data and Foundation Modelcited by 0
  35. 2026TaskBridge: Bridging Unsupervised Tabular Anomaly Detection and In-Context Learning via Virtual Taskscited by 0
  36. 2026On the Divergence of Accuracy and Mechanism Consistency in Time Series World Modelscited by 0
  37. 2026RideBench: A Large-Scale Exogenous-Aware Benchmark for Ride-Hailing Time Series Forecastingcited by 0