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eulogik/nanoforecast-patchtst-baselines

sourceHugging Faceapache-2.0updated 24d agoView on Hugging Face

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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

Parameter	Value
d_model	512
e_layers	3
patch_len	16
stride	8
dropout	0.3
learning rate	1e-4
patience	3
lookback	512
horizon	48

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

Dataset	MASE	MAE	MSE	sMAPE
ETTh1	0.781	0.574	0.342	5.74
ETTh2	1.467	0.907	0.684	4.39
ETTm1	0.488	0.299	0.210	3.59
exchange_rate	3.861	0.020	0.001	1.19
electricity	1.347	48.718	42.173	1.41
traffic	1.379	0.011	0.000	43.23

Usage

python

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

Citation

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

bibtex

@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](https://eulogik.com) for [NanoForecast](https://github.com/eulogik/NanoForecast) benchmarking.