granite-timeseries-patchtst

PatchTST model pre-trained on ETTh1 dataset#

PatchTST is a transformer-based model for time series modeling tasks, including forecasting, regression, and classification. This repository contains a pre-trained PatchTST model encompassing all seven channels of the ETTh1 dataset. This particular pre-trained model produces a Mean Squared Error (MSE) of 0.3881 on the test split of the ETTh1 dataset when forecasting 96 hours into the future with a historical data window of 512 hours.

For training and evaluating a PatchTST model, you can refer to this demo notebook.

Model Details#

Model Description#

The PatchTST model was proposed in A Time Series is Worth 64 Words: Long-term Forecasting with Transformers by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam.

At a high level the model vectorizes time series into patches of a given size and encodes the resulting sequence of vectors via a Transformer that then outputs the prediction length forecast via an appropriate head.

The model is based on two key components: (i) segmentation of time series into subseries-level patches which are served as input tokens to Transformer; (ii) channel-independence where each channel contains a single univariate time series that shares the same embedding and Transformer weights across all the series. The patching design naturally has three-fold benefit: local semantic information is retained in the embedding; computation and memory usage of the attention maps are quadratically reduced given the same look-back window; and the model can attend longer history. Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models.

In addition, PatchTST has a modular design to seamlessly support masked time series pre-training as well as direct time series forecasting, classification, and regression.

Image: Architecture

Model Sources#

• Repository: PatchTST Hugging Face
• Paper: PatchTST ICLR 2023 paper
• Demo: Get started with PatchTST

Uses#

This pre-trained model can be employed for fine-tuning or evaluation using any Electrical Transformer dataset that has the same channels as the ETTh1 dataset, specifically: HUFL, HULL, MUFL, MULL, LUFL, LULL, OT. The model is designed to predict the next 96 hours based on the input values from the preceding 512 hours. It is crucial to normalize the data. For a more comprehensive understanding of data pre-processing, please consult the paper or the demo.

How to Get Started with the Model#

Use the code below to get started with the model.

Demo

Training Details#

Training Data#

ETTh1/train split. Train/validation/test splits are shown in the demo.

Training hyperparameters#

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training Results#

Evaluation#

Testing Data#

ETTh1/test split. Train/validation/test splits are shown in the demo.

Metrics#

Mean Squared Error (MSE).

Results#

It achieves a MSE of 0.3881 on the evaluation dataset.

Hardware#

1 NVIDIA A100 GPU

Framework versions#

• Transformers 4.36.0.dev0
• Pytorch 2.0.1
• Datasets 2.14.4
• Tokenizers 0.14.1

Citation#

BibTeX:

@misc{nie2023time,
      title={A Time Series is Worth 64 Words: Long-term Forecasting with Transformers}, 
      author={Yuqi Nie and Nam H. Nguyen and Phanwadee Sinthong and Jayant Kalagnanam},
      year={2023},
      eprint={2211.14730},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

APA:

Nie, Y., Nguyen, N., Sinthong, P., & Kalagnanam, J. (2023). A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. arXiv preprint arXiv:2211.14730.