nanoforecast-patchtst-baselines

PatchTST Baselines (Standard Protocol)

Channel-independent PatchTST baselines trained on a free Colab T4 with the exact hyperparameters from the PatchTST paper. Used as reference baselines for NanoForecast standard-protocol benchmarking.

What is This?

These are reference baselines for comparing against NanoForecast. Each model was trained independently on a single dataset (channel-independent: each channel is a separate sample).

Training Configuration

Files

• {ds}.pt — trained model weights (PyTorch)
• {ds}.json — per-dataset benchmark results
• standard_benchmark.json — aggregated MASE/MAE/MSE/sMAPE under the protocol in benchmark_standard.py

Benchmark Results

Usage

import torch
model = torch.load("ETTh1.pt")

Citation

If you use these baselines, please cite the original PatchTST paper:

@inproceedings{nie2023time,
  title={A Time Series is Worth 64 Words: Long-term Forecasting with Transformers},
  author={Nie, Yuqi and Nguyen, Nam H and Sinthong, Phanwadee and Kalagnanam, Jayant},
  booktitle={International Conference on Learning Representations},
  year={2023}
}

---

Baselines generated by Eulogik for NanoForecast benchmarking.