update model card

README.md

@@ -1,33 +1,70 @@
 ---
 tags:
 - generated_from_trainer
+license: cdla-permissive-2.0
 model-index:
 - name: patchtst_etth1_forecast
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# PatchTST model pre-trained on ETTh1 dataset
 
-# patchtst_etth1_forecast
+<!-- Provide a quick summary of what the model is/does. -->
 
-This model was trained from scratch on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.3881
+[`PatchTST`](https://huggingface.co/docs/transformers/model_doc/patchtst) is a transformer-based model for time series modeling tasks, including forecasting, regression, and classification.
 
-## Model description
 
-More information needed
+In this context, we offer 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.
 
-## Intended uses & limitations
+For training and evaluating a `PatchTST` model, you can refer to [this notebook](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/patch_tst_getting_started.ipynb).
 
-More information needed
+## Model Details
 
-## Training and evaluation data
+The `PatchTST` model was proposed in A Time Series is Worth [64 Words: Long-term Forecasting with Transformers](https://arxiv.org/abs/2211.14730) by Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, Jayant Kalagnanam.
 
-More information needed
+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.
+
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+<img src="patchtst_architecture.png" alt="Architecture" width="600" />
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [PatchTST Hugging Face](https://huggingface.co/docs/transformers/model_doc/patchtst)
+- **Paper:** [PatchTST ICLR 2023 paper](https://dl.acm.org/doi/abs/10.1145/3580305.3599533)
+- **Demo:** [Get started with PatchTST](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/patch_tst_getting_started.ipynb)
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+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](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/patch_tst_getting_started.ipynb)
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[`ETTh1`/train split](https://github.com/zhouhaoyi/ETDataset/blob/main/ETT-small/ETTh1.csv). 
+Train/validation/test splits are shown in the [demo](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/patch_tst_getting_started.ipynb).
 
-## Training procedure
 
 ### Training hyperparameters
 
@@ -40,7 +77,7 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 10
 
-### Training results
+### Training Results
 
 | Training Loss | Epoch | Step  | Validation Loss |
 |:-------------:|:-----:|:-----:|:---------------:|
@@ -55,10 +92,53 @@ The following hyperparameters were used during training:
 | 0.3053        | 9.0   | 9045  | 0.8199          |
 | 0.3019        | 10.0  | 10050 | 0.8173          |
 
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data
+
+[`ETTh1`/test split](https://github.com/zhouhaoyi/ETDataset/blob/main/ETT-small/ETTh1.csv). 
+Train/validation/test splits are shown in the [demo](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/patch_tst_getting_started.ipynb).
+
+### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+Mean Squared Error (MSE).
 
-### Framework versions
+### 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
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**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.
+```