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PatchTST (ETTh1)

Model directory
ibm-research/granite-timeseries-patchtst

616K params | Apache-2.0 | Upstream checkpoint

This checkpoint is IBM's upstream PatchTST reference model for ETTh1 rather than a broad multi-domain TSFM family card.

At 616K parameters it uses patch tokenization and Transformer blocks to model long-horizon time-series behavior efficiently. The catch is the training scope: per the official model card, the checkpoint is trained on the ETTh1 train split covering all seven ETTh1 channels, and that same seven-series target shape is what it expects at inference time. It is tuned around the exact ETTh1 setup instead of generic univariate forecasting, so it does not transfer to arbitrary series the way the foundation models here do.

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Hugging Face provider mappings checked 2026-10-06. No Inference Provider mapping was listed for this checkpoint.

Model Classification

Family

PatchTST

Type

pretrained forecasting model

Pretrained forecasting reference checkpoint, but not positioned upstream as a general-purpose TSFM family.

Training Data

ETTh1 train split covering all seven ETTh1 channels, per the official model card. This upstream checkpoint expects that same seven-series target shape at inference time.

Recommended For

  • • Reference long-horizon forecasting baselines
  • • Interpretable comparisons against other TSFMs

Strengths

  • • Strong and widely cited long-horizon patch-transformer baseline
  • • Easy comparison point for benchmark readers

Limitations

  • • This upstream checkpoint is dataset-specific rather than a universal foundation model
  • • Narrower operational scope than the broader TSFM families in the catalog

Capabilities

forecastingmultivariatelong-horizon

Tags

ibmpatchtstbaselinelong-horizon

Specifications

Parameters
616K
Architecture
PatchTST transformer with patch tokenization
Context length
n/a
Max context
n/a
Minimum history
n/a
Recommended history
n/a
Input step
n/a
Required target series
1
Temperature
Ignored
Top P
Ignored
Max output
n/a
License
Apache-2.0