Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.15802 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912969285173248 |
|---|---|
| author | Potapczynski, Andres Selvam, Ravi Kiran Konstantinova, Tatiana Ramasubramanian, Shankar Wolff, Malcolm Olivares, Kin G. Ma, Ruijun Cao, Mengfei Mahoney, Michael W. Wilson, Andrew Gordon Oreshkin, Boris N. Efimov, Dmitry |
| author_facet | Potapczynski, Andres Selvam, Ravi Kiran Konstantinova, Tatiana Ramasubramanian, Shankar Wolff, Malcolm Olivares, Kin G. Ma, Ruijun Cao, Mengfei Mahoney, Michael W. Wilson, Andrew Gordon Oreshkin, Boris N. Efimov, Dmitry |
| contents | In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load; calendar and holiday indicators for traffic or sales; and grid load or fuel costs in electricity pricing. Ignoring these exogenous signals can substantially degrade forecasting accuracy, particularly when they drive spikes, discontinuities, or regime and phase changes in the target series. Most current time series foundation models (e.g., Chronos, Sundial, TimesFM, TimeMoE, TimeLLM, and LagLlama) ignore exogenous covariates and make forecasts solely from the numerical time series history, thereby limiting their performance. In this paper, we develop ApolloPFN, a prior-data fitted network (PFN) that is time-aware (unlike prior PFNs) and that natively incorporates exogenous covariates (unlike prior univariate forecasters). Our design introduces two major advances: (i) a synthetic data generation procedure tailored to resolve the failure modes that arise when tabular (non-temporal) PFNs are applied to time series; and (ii) time-aware architectural modifications that embed inductive biases needed to exploit the time series context. We demonstrate that ApolloPFN achieves state-of-the-art results across benchmarks, such as M5 and electric price forecasting, that contain exogenous information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15802 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables Potapczynski, Andres Selvam, Ravi Kiran Konstantinova, Tatiana Ramasubramanian, Shankar Wolff, Malcolm Olivares, Kin G. Ma, Ruijun Cao, Mengfei Mahoney, Michael W. Wilson, Andrew Gordon Oreshkin, Boris N. Efimov, Dmitry Machine Learning In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load; calendar and holiday indicators for traffic or sales; and grid load or fuel costs in electricity pricing. Ignoring these exogenous signals can substantially degrade forecasting accuracy, particularly when they drive spikes, discontinuities, or regime and phase changes in the target series. Most current time series foundation models (e.g., Chronos, Sundial, TimesFM, TimeMoE, TimeLLM, and LagLlama) ignore exogenous covariates and make forecasts solely from the numerical time series history, thereby limiting their performance. In this paper, we develop ApolloPFN, a prior-data fitted network (PFN) that is time-aware (unlike prior PFNs) and that natively incorporates exogenous covariates (unlike prior univariate forecasters). Our design introduces two major advances: (i) a synthetic data generation procedure tailored to resolve the failure modes that arise when tabular (non-temporal) PFNs are applied to time series; and (ii) time-aware architectural modifications that embed inductive biases needed to exploit the time series context. We demonstrate that ApolloPFN achieves state-of-the-art results across benchmarks, such as M5 and electric price forecasting, that contain exogenous information. |
| title | Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.15802 |