TempoPFN: Synthetic Pre-Training of Linear RNNs for Zero-Shot Time Series Forecasting

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TempoPFN introduced in TempoPFN: Synthetic Pre-Training of Linear RNNs for Zero-Shot Time Series Forecasting, is a univariate time series foundation model pretrained entirely on synthetic data. It delivers top-tier zero-shot forecasting accuracy while remaining fully reproducible and free from real-data leakage.

Built on a Linear RNN (GatedDeltaProduct) backbone, TempoPFN performs end-to-end forecasting without patching or windowing. Its design enables fully parallelizable training and inference while maintaining stable temporal state-tracking across long sequences. The GatedDeltaProduct architecture is based on DeltaProduct, extended with state-weaving for time series forecasting. For detailed information about the architecture and custom modifications, see src/models/gated_deltaproduct/README.md.

This repository includes the pretrained 38M parameter model,, all training and inference code, and the complete synthetic data generation pipeline used for pretraining.

✨ Why TempoPFN?

• High Performance, No Real Data: Achieves top-tier competitive results on GIFT-Eval, outperforming all existing synthetic-only approaches and surpassing the vast majority of models trained on real-world data. This ensures full reproducibility and eliminates benchmark leakage.
• Parallel and Efficient: The linear recurrence design enables full-sequence parallelization. This gives us the best of both worlds: the linear efficiency of an RNN, but with the training parallelism of a Transformer.
• Open and Reproducible: Includes the full synthetic data pipeline, configurations, and scripts to reproduce training from scratch.
• State-Tracking Stability: The GatedDeltaProduct recurrence and state-weaving mechanism preserve temporal continuity and information flow across long horizons, improving robustness without non-linear recurrence.

⚙️ Installation

git clone https://github.com/automl/TempoPFN.git
cd TempoPFN
python -m venv venv && source venv/bin/activate

# 1. Install PyTorch version matching the CUDA version
# Example for CUDA 12.6:
pip install torch --index-url https://download.pytorch.org/whl/cu126

# 2. Install TempoPFN and all other dependencies
pip install .
export PYTHONPATH=$PWD

🚀 Quick Start: Run the Demo

Prerequisites:

• You must have a CUDA-capable GPU with a matching PyTorch version installed.
• You have run export PYTHONPATH=$PWD from the repo's root directory (see Installation).

1. Run the Quick Start Script

Run a demo forecast on a synthetic sine wave:

python examples/quick_start_tempo_pfn.py

2. Run with a Local Checkpoint

If you have already downloaded the model (e.g., to models/checkpoint.pth), you can point the script to it:

python examples/quick_start_tempo_pfn.py --checkpoint models/checkpoint.pth

3. Run the Notebook version

jupyter notebook examples/quick_start_tempo_pfn.ipynb

Hardware & Performance Tips

GPU Required: Inference requires a CUDA-capable GPU. Tested on NVIDIA A100/H100.

First Inference May Be Slow: Initial calls for unseen sequence lengths trigger Triton kernel compilation. Subsequent runs are cached and fast.

Triton Caches: To prevent slowdowns from writing caches to a network filesystem, route caches to a local directory (like /tmp) before running:

LOCAL_CACHE_BASE="${TMPDIR:-/tmp}/tsf-$(date +%s)"
mkdir -p "${LOCAL_CACHE_BASE}/triton" "${LOCAL_CACHE_BASE}/torchinductor"
export TRITON_CACHE_DIR="${LOCAL_CACHE_BASE}/triton"
export TORCHINDUCTOR_CACHE_DIR="${LOCAL_CACHE_BASE}/torchinductor"

python examples/quick_start_tempo_pfn.py

🚂 Training

Single-GPU Training (for debugging)

torchrun --standalone --nproc_per_node=1 src/training/trainer_dist.py --config ./configs/train.yaml

Multi-GPU Training (Single-Node)

This example uses 8 GPUs. The training script uses PyTorch DistributedDataParallel (DDP).

torchrun --standalone --nproc_per_node=8 src/training/trainer_dist.py --config ./configs/train.yaml

Configuration

All training and model parameters are controlled via YAML files in configs/ (architecture, optimizers, paths).

💾 Synthetic Data Generation

A core contribution of this work is our open-source synthetic data pipeline, located in src/synthetic_generation/. It combines diverse generators with a powerful augmentation cascade.

Generators Used:

• Adapted Priors: ForecastPFN, KernelSynth, GaussianProcess (GP), and CauKer (Structural Causal Models).
• Novel Priors: SDE (a flexible regime-switching Ornstein-Uhlenbeck process), Sawtooth, StepFunction, Anomaly, Spikes, SineWave, and Audio-Inspired generators (Stochastic Rhythms, Financial Volatility, Network Topology, Multi-Scale Fractals).

You can easily generate your own data by installing the development dependencies and instantiating a generator wrapper. See examples/generate_synthetic_data.py for a minimal script, or inspect the generator code in src/synthetic_generation/.

🤝 License

This project is licensed under the Apache 2.0 License. See the LICENSE file for details. This permissive license allows for both academic and commercial use.

📚 Citation

If you find TempoPFN useful in your research, please consider citing our paper:

@misc{moroshan2025tempopfn,
  title={TempoPFN: Synthetic Pre-Training of Linear RNNs for Zero-Shot Time Series Forecasting},
  author={Vladyslav Moroshan and Julien Siems and Arber Zela and Timur Carstensen and Frank Hutter},
  year={2025},
  eprint={2510.25502},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}