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

arXiv: https://arxiv.org/abs/2501.02945 · HTML: https://mlsanity.com/paper/2501.02945/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 6 Jan 2025.

## Builds on (most cited first, 10 of 10)

- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation](https://mlsanity.com/paper/2410.10393/) (2024)
- [TabDPT: Scaling Tabular Foundation Models on Real Data](https://mlsanity.com/paper/2410.18164/) (2024)
- [Toto: Time Series Optimized Transformer for Observability](https://mlsanity.com/paper/2407.07874/) (2024)
- [AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2308.05566/) (2023)

## Built on by (most cited first, 30 of 60)

- [Chronos-2: From Univariate to Universal Forecasting](https://mlsanity.com/paper/2510.15821/) (2025)
- [TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems](https://mlsanity.com/paper/2502.02527/) (2025)
- [Turning Tabular Foundation Models into Graph Foundation Models](https://mlsanity.com/paper/2508.20906/) (2025)
- [Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model](https://mlsanity.com/paper/2506.11029/) (2025)
- [Bringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Models](https://mlsanity.com/paper/2509.07143/) (2025)
- [Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models](https://mlsanity.com/paper/2503.11411/) (2025)
- [Evaluating System 1 vs. 2 Reasoning Approaches for Zero-Shot Time Series Forecasting: A Benchmark and Insights](https://mlsanity.com/paper/2503.01895/) (2025)
- [TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models](https://mlsanity.com/paper/2511.02802/) (2025)
- [Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?](https://mlsanity.com/paper/2512.08798/) (2025)
- [Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling](https://mlsanity.com/paper/2605.27286/) (2026)
- [TimeCopilot](https://mlsanity.com/paper/2509.00616/) (2025)
- [CoRA: Covariate-Aware Adaptation of Time Series Foundation Models](https://mlsanity.com/paper/2510.12681/) (2025)
- [Realistic Evaluation of TabPFN v2 in Open Environments](https://mlsanity.com/paper/2505.16226/) (2025)
- [Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models](https://mlsanity.com/paper/2510.02224/) (2025)
- [From Tables to Signals: Revealing Spectral Adaptivity in TabPFN](https://mlsanity.com/paper/2511.18278/) (2025)
- [TempusBench: An Evaluation Framework for Time-Series Forecasting](https://mlsanity.com/paper/2604.11529/) (2026)
- [RocketPFN: Accurate Time Series Classification via In-Context Learning](https://mlsanity.com/paper/2606.21786/) (2026)
- [One-Embedding-Fits-All: Efficient Zero-Shot Time Series Forecasting by a Model Zoo](https://mlsanity.com/paper/2509.04208/) (2025)
- [TiCT: A Synthetically Pre-Trained Foundation Model for Time Series Classification](https://mlsanity.com/paper/2511.19694/) (2025)
- [Reasoning-Aware Training for Time Series Forecasting](https://mlsanity.com/paper/2605.08625/) (2026)
- [FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models](https://mlsanity.com/paper/2605.09081/) (2026)
- [TabFORGE: A Tabular Foundation Model for Generative Modelling](https://mlsanity.com/paper/2605.09424/) (2026)
- [Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks](https://mlsanity.com/paper/2605.10590/) (2026)
- [SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference](https://mlsanity.com/paper/2605.15488/) (2026)
- [Into the ORBIT for Time Series: Training Regimes for Foundation Models](https://mlsanity.com/paper/2608.13262/) (2026)
- [t0: A Time-Series Foundation Model for Forecasting with Context](https://mlsanity.com/paper/2609.24559/) (2026)
- [Are Time-Indexed Foundation Models the Future of Time Series Imputation?](https://mlsanity.com/paper/2511.05980/) (2025)
- [In-Context Learning Under Regime Change](https://mlsanity.com/paper/2604.16988/) (2026)
- [RareCP: Regime-Aware Retrieval for Efficient Conformal Prediction](https://mlsanity.com/paper/2605.08857/) (2026)
- [Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting](https://mlsanity.com/paper/2606.07457/) (2026)

## 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.