| Name | torch-timeseries JSON |
| Version |
0.1.5
JSON |
| download |
| home_page | https://github.com/wayne155/pytorch_timeseries |
| Summary | Timeseries Learning Library for PyTorch. |
| upload_time | 2024-11-24 12:00:21 |
| maintainer | None |
| docs_url | None |
| author | Weiwei Ye |
| requires_python | >=3.8 |
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| keywords |
deep learning
time series
pytorch
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
numpy
pandas
tqdm
fire
sktime
scikit-learn
prettytable
einops
torchmetrics
fire
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[pypi-image]: https://badge.fury.io/py/torch-timeseries.svg
[pypi-url]: https://pypi.python.org/pypi/torch-timeseries
[docs-image]: https://readthedocs.org/projects/pytorch-timeseries/badge/?version=latest
[docs-url]: https://pytorch-timeseries.readthedocs.io/en/latest/?badge=latest
<p align="center">
<img width="90%" src="https://raw.githubusercontent.com/wayne155/pytorch_timeseries/main/docs/_static/img/logo_text.jpg?sanitize=true" />
</p>
[![PyPI Version][pypi-image]][pypi-url]
[![Docs Status][docs-image]][docs-url]
# pytorch_timeseries
An all in one deep learning library that boost your timeseries research.
[Check the documentation for more detail](https://pytorch-timeseries.readthedocs.io/en/latest/).
Compared to previous libraries, pytorch_timeseries is
- dataset automatically downloaded
- easy to use and extend
- clear documentation
- highly customizable
- install and run!
- ..........
## 1. installation
```
pip install torch-timeseries
```
> ⚠️⚠️⚠️ **Warning: We only support python version >= 3.8+**
## 2. Running Implemented Experiments
### Forecast
```python
# running DLinear Forecast on dataset ETTh1 with seed = 3
pytexp --model DLinear --task Forecast --dataset_type ETTh1 run 3
# running DLinear Forecast on dataset ETTh1 with seeds=[1,2,3]
pytexp --model DLinear --task Forecast --dataset_type ETTh1 runs '[1,2,3]'
```
### Imputation
```python
# running DLinear Imputation on dataset ETTh1 with seed = 3
pytexp --model DLinear --task Imputation --dataset_type ETTh1 run 3
# running DLinear Imputation on dataset ETTh1 with seed = [1,2,3]
pytexp --model DLinear --task Imputation --dataset_type ETTh1 runs '[1,2,3]'
```
### UEAClassification
```python
# running DLinear UEAClassification on dataset EthanolConcentration with seed = 3
pytexp --model DLinear --task UEAClassification --dataset_type EthanolConcentration run 3
# running DLinear UEAClassification on dataset EthanolConcentration with seed = [1,2,3]
pytexp --model DLinear --task UEAClassification --dataset_type EthanolConcentration runs '[1,2,3]'
```
### AnomalyDetection
```python
# running DLinear AnomalyDetection on dataset MSL with seed = [1,2,3]
pytexp --model DLinear --task AnomalyDetection --dataset_type MSL run 3
# running DLinear AnomalyDetection on dataset MSL with seed = [1,2,3]
pytexp --model DLinear --task AnomalyDetection --dataset_type MSL runs 3
```
# Development Milestones
## Implemented Datasets
Full list of datasets can be found at [Documentation](https://pytorch-timeseries.readthedocs.io/en/latest/modules/dataset.html).
| Datasets | Forecasting | Imputation | Anomaly | Classification|
| --------- | ------- | ------- | ------- | ------- |
| [ETTh1](https://ojs.aaai.org/index.php/AAAI/article/view/17325) | ✅ |✅ | | |
| [ETTh2](https://ojs.aaai.org/index.php/AAAI/article/view/17325) | ✅ |✅ | | |
| [ETTm1](https://ojs.aaai.org/index.php/AAAI/article/view/17325) | ✅ |✅ | | |
| [ETTm2](https://ojs.aaai.org/index.php/AAAI/article/view/17325) | ✅ |✅ | | |
| [......And More](https://pytorch-timeseries.readthedocs.io/en/latest/modules/dataset.html) | ✅ |✅ | ✅ | ✅ |
## Implemented Tasks
- [x] Forecast
- [x] Classfication (for UEA datasets)
- [x] Anomaly Detection
- [x] Imputation
- [ ] You can fill this check box! (contribute to develop your own task!)
## Implemented Models
| Models | Forecasting | Imputation | Anomaly | Classification|
| --------- | ------- | ------- | ------- | ------- |
| [Informer (2021)](https://ojs.aaai.org/index.php/AAAI/article/view/17325) | ✅ |✅ |✅ |✅ |
| [Autoformer (2021)](https://proceedings.neurips.cc/paper/2021/hash/bcc0d400288793e8bdcd7c19a8ac0c2b-Abstract.html) | ✅ |✅ |✅ |✅ |
| [FEDformer (2022)](https://proceedings.mlr.press/v162/zhou22g.html) | ✅ |✅ |✅ |✅ |
| [DLinear (2022)](https://ojs.aaai.org/index.php/AAAI/article/view/26317) | ✅ |✅ |✅ |✅ |
| [PatchTST (2022)](https://openreview.net/forum?id=Jbdc0vTOcol&trk=public_post_comment-text) | ✅ |✅ |✅ |✅ |
| [iTransformer (2024)](https://openreview.net/forum?id=JePfAI8fah) | ✅ |✅ |✅ |✅ |
<!-- ## Implemented Datasets
Currently we have implemented all popular datasets, including
| Datasets | Forecasting | Imputation | Anomaly | Classification|
| --------- | ------- | ------- | ------- | ------- |
| [ETTh1](https://ojs.aaai.org/index.php/AAAI/article/view/26317) | ✅ |✅ | | |
| [ETTh2](https://ojs.aaai.org/index.php/AAAI/article/view/26317) | ✅ |✅ | | |
| [ETTm1](https://ojs.aaai.org/index.php/AAAI/article/view/26317) | ✅ |✅ | | |
| [ETTm2](https://ojs.aaai.org/index.php/AAAI/article/view/26317) | ✅ |✅ | | |
[Check the documentation for more detail](https://pytorch-timeseries.readthedocs.io/en/latest/).
-->
# Customizing Your Own Pipeline
we provide examples of :
- [forecast](https://github.com/wayne155/pytorch_timeseries/blob/main/examples/forecast.py)
- [imputation](https://github.com/wayne155/pytorch_timeseries/blob/main/examples/mask.py)
- [anomaly detection](https://github.com/wayne155/pytorch_timeseries/blob/main/examples/anomaly.py)
- [UEA classfication](https://github.com/wayne155/pytorch_timeseries/blob/main/examples/ueaclass.py)
Detail of customize forecasting pipeline is as follows:
## 1 Forecasting
### 1.1 download dataset
The dataset will be downloaded **automatically!!!!**
```python
from torch_timeseries.dataset import ETTh1
from torch_timeseries.dataloader import StandardScaler, SlidingWindow, SlidingWindowTS
from torch_timeseries.model import DLinear
from torch.nn import MSELoss, L1Loss
from torch.optim import Adam
dataset = ETTh1('./data')
```
### 1.2 setup scaler/dataloader
Once you setup a dataloader and pass a scaler into this dataloader, the scaler will be fitted on the training set.
```python
scaler = StandardScaler()
dataloader = SlidingWindowTS(dataset,
window=96,
horizon=1,
steps=336,
batch_size=32,
train_ratio=0.7,
val_ratio=0.2,
scaler=scaler,
)
```
After this, you can access the train/val/test loader by `dataloader.train_loader/val_loader/test_loader`
### 1.3 training
```python
model = DLinear(dataloader.window, dataloader.steps, dataset.num_features, individual= True)
optimizer = Adam(model.parameters())
loss_function = MSELoss()
# train
model.train()
for scaled_x, scaled_y, x, y, x_date_enc, y_date_enc in dataloader.train_loader:
optimizer.zero_grad()
scaled_x = scaled_x.float()
scaled_y = scaled_y.float()
scaled_pred_y = model(scaled_x)
loss = loss_function(scaled_pred_y, scaled_y)
loss.backward()
optimizer.step()
print(loss)
```
### 1.4 val/test
```python
# val
model.eval()
for scaled_x, scaled_y, x, y, x_date_enc, y_date_enc in dataloader.val_loader:
....your validation code here...
# test
model.eval()
for scaled_x, scaled_y, x, y, x_date_enc, y_date_enc in dataloader.test_loader:
....your test code here...
```
# Dev Install
## install requirements
> Note:This library assumes that you've installed Pytorch according to it's official website, the basic dependencies of torch > > related libraries may not be listed in the requirements files:
https://pytorch.org/get-started/locally/
**The recommended python version is 3.8.1+.**
1. fork this project
2. clone this project (latest version)
```
git clone https://github.com/wayne155/pytorch_timeseries
```
3. install requirements.
```
pip install -r ./requirements.txt
```
4. change some code and push to the forked repo
5. create a pull request to this repo
Raw data
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"maintainer": null,
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"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "deep Learning, time series, pytorch",
"author": "Weiwei Ye",
"author_email": "Wayne Yip <wwye155@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/bd/7c/00e17ac582b001b72aad51425406f78b7c16bb2682aff460fbb926ac858f/torch_timeseries-0.1.5.tar.gz",
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"description": "[pypi-image]: https://badge.fury.io/py/torch-timeseries.svg\n[pypi-url]: https://pypi.python.org/pypi/torch-timeseries\n[docs-image]: https://readthedocs.org/projects/pytorch-timeseries/badge/?version=latest\n[docs-url]: https://pytorch-timeseries.readthedocs.io/en/latest/?badge=latest\n\n\n\n<p align=\"center\">\n <img width=\"90%\" src=\"https://raw.githubusercontent.com/wayne155/pytorch_timeseries/main/docs/_static/img/logo_text.jpg?sanitize=true\" />\n</p>\n\n[![PyPI Version][pypi-image]][pypi-url]\n[![Docs Status][docs-image]][docs-url]\n\n\n\n# pytorch_timeseries\nAn all in one deep learning library that boost your timeseries research.\n[Check the documentation for more detail](https://pytorch-timeseries.readthedocs.io/en/latest/).\n\n\n\nCompared to previous libraries, pytorch_timeseries is \n- dataset automatically downloaded\n- easy to use and extend \n- clear documentation \n- highly customizable \n- install and run! \n- ..........\n\n\n\n## 1. installation\n\n\n\n```\npip install torch-timeseries\n```\n\n> \u26a0\ufe0f\u26a0\ufe0f\u26a0\ufe0f **Warning: We only support python version >= 3.8+**\n\n\n## 2. Running Implemented Experiments\n\n### Forecast\n```python\n# running DLinear Forecast on dataset ETTh1 with seed = 3 \npytexp --model DLinear --task Forecast --dataset_type ETTh1 run 3\n# running DLinear Forecast on dataset ETTh1 with seeds=[1,2,3]\npytexp --model DLinear --task Forecast --dataset_type ETTh1 runs '[1,2,3]'\n```\n\n\n### Imputation\n```python\n# running DLinear Imputation on dataset ETTh1 with seed = 3 \npytexp --model DLinear --task Imputation --dataset_type ETTh1 run 3\n# running DLinear Imputation on dataset ETTh1 with seed = [1,2,3] \npytexp --model DLinear --task Imputation --dataset_type ETTh1 runs '[1,2,3]'\n```\n### UEAClassification\n```python\n# running DLinear UEAClassification on dataset EthanolConcentration with seed = 3 \npytexp --model DLinear --task UEAClassification --dataset_type EthanolConcentration run 3\n# running DLinear UEAClassification on dataset EthanolConcentration with seed = [1,2,3] \npytexp --model DLinear --task UEAClassification --dataset_type EthanolConcentration runs '[1,2,3]'\n```\n\n### AnomalyDetection\n```python\n# running DLinear AnomalyDetection on dataset MSL with seed = [1,2,3] \npytexp --model DLinear --task AnomalyDetection --dataset_type MSL run 3\n# running DLinear AnomalyDetection on dataset MSL with seed = [1,2,3] \npytexp --model DLinear --task AnomalyDetection --dataset_type MSL runs 3\n```\n\n\n# Development Milestones\n## Implemented Datasets\nFull list of datasets can be found at [Documentation](https://pytorch-timeseries.readthedocs.io/en/latest/modules/dataset.html).\n| Datasets | Forecasting | Imputation | Anomaly | Classification|\n| --------- | ------- | ------- | ------- | ------- |\n| [ETTh1](https://ojs.aaai.org/index.php/AAAI/article/view/17325) | \u2705 |\u2705 | | |\n| [ETTh2](https://ojs.aaai.org/index.php/AAAI/article/view/17325) | \u2705 |\u2705 | | |\n| [ETTm1](https://ojs.aaai.org/index.php/AAAI/article/view/17325) | \u2705 |\u2705 | | |\n| [ETTm2](https://ojs.aaai.org/index.php/AAAI/article/view/17325) | \u2705 |\u2705 | | |\n| [......And More](https://pytorch-timeseries.readthedocs.io/en/latest/modules/dataset.html) | \u2705 |\u2705 | \u2705 | \u2705 |\n\n## Implemented Tasks\n\n- [x] Forecast\n- [x] Classfication (for UEA datasets)\n- [x] Anomaly Detection \n- [x] Imputation\n- [ ] You can fill this check box! (contribute to develop your own task!)\n\n## Implemented Models\n\n| Models | Forecasting | Imputation | Anomaly | Classification|\n| --------- | ------- | ------- | ------- | ------- |\n| [Informer (2021)](https://ojs.aaai.org/index.php/AAAI/article/view/17325) | \u2705 |\u2705 |\u2705 |\u2705 |\n| [Autoformer (2021)](https://proceedings.neurips.cc/paper/2021/hash/bcc0d400288793e8bdcd7c19a8ac0c2b-Abstract.html) | \u2705 |\u2705 |\u2705 |\u2705 |\n| [FEDformer (2022)](https://proceedings.mlr.press/v162/zhou22g.html) | \u2705 |\u2705 |\u2705 |\u2705 |\n| [DLinear (2022)](https://ojs.aaai.org/index.php/AAAI/article/view/26317) | \u2705 |\u2705 |\u2705 |\u2705 |\n| [PatchTST (2022)](https://openreview.net/forum?id=Jbdc0vTOcol&trk=public_post_comment-text) | \u2705 |\u2705 |\u2705 |\u2705 |\n| [iTransformer (2024)](https://openreview.net/forum?id=JePfAI8fah) | \u2705 |\u2705 |\u2705 |\u2705 |\n\n<!-- ## Implemented Datasets\nCurrently we have implemented all popular datasets, including\n\n| Datasets | Forecasting | Imputation | Anomaly | Classification|\n| --------- | ------- | ------- | ------- | ------- |\n| [ETTh1](https://ojs.aaai.org/index.php/AAAI/article/view/26317) | \u2705 |\u2705 | | |\n| [ETTh2](https://ojs.aaai.org/index.php/AAAI/article/view/26317) | \u2705 |\u2705 | | |\n| [ETTm1](https://ojs.aaai.org/index.php/AAAI/article/view/26317) | \u2705 |\u2705 | | |\n| [ETTm2](https://ojs.aaai.org/index.php/AAAI/article/view/26317) | \u2705 |\u2705 | | |\n\n[Check the documentation for more detail](https://pytorch-timeseries.readthedocs.io/en/latest/).\n -->\n\n# Customizing Your Own Pipeline\n\nwe provide examples of :\n- [forecast](https://github.com/wayne155/pytorch_timeseries/blob/main/examples/forecast.py)\n- [imputation](https://github.com/wayne155/pytorch_timeseries/blob/main/examples/mask.py)\n- [anomaly detection](https://github.com/wayne155/pytorch_timeseries/blob/main/examples/anomaly.py)\n- [UEA classfication](https://github.com/wayne155/pytorch_timeseries/blob/main/examples/ueaclass.py)\n\nDetail of customize forecasting pipeline is as follows:\n\n## 1 Forecasting\n\n### 1.1 download dataset\nThe dataset will be downloaded **automatically!!!!**\n```python\nfrom torch_timeseries.dataset import ETTh1\nfrom torch_timeseries.dataloader import StandardScaler, SlidingWindow, SlidingWindowTS\nfrom torch_timeseries.model import DLinear\nfrom torch.nn import MSELoss, L1Loss\nfrom torch.optim import Adam\ndataset = ETTh1('./data')\n```\n\n### 1.2 setup scaler/dataloader\n\nOnce you setup a dataloader and pass a scaler into this dataloader, the scaler will be fitted on the training set.\n\n\n```python\nscaler = StandardScaler()\ndataloader = SlidingWindowTS(dataset, \n window=96,\n horizon=1,\n steps=336,\n batch_size=32, \n train_ratio=0.7, \n val_ratio=0.2, \n scaler=scaler,\n )\n\n```\nAfter this, you can access the train/val/test loader by `dataloader.train_loader/val_loader/test_loader` \n\n### 1.3 training\n\n\n\n```python\nmodel = DLinear(dataloader.window, dataloader.steps, dataset.num_features, individual= True)\noptimizer = Adam(model.parameters())\nloss_function = MSELoss()\n\n# train\nmodel.train()\nfor scaled_x, scaled_y, x, y, x_date_enc, y_date_enc in dataloader.train_loader:\n optimizer.zero_grad()\n \n scaled_x = scaled_x.float()\n scaled_y = scaled_y.float()\n scaled_pred_y = model(scaled_x) \n \n loss = loss_function(scaled_pred_y, scaled_y)\n loss.backward()\n optimizer.step()\n print(loss)\n```\n\n### 1.4 val/test\n\n```python\n# val\nmodel.eval()\nfor scaled_x, scaled_y, x, y, x_date_enc, y_date_enc in dataloader.val_loader:\n ....your validation code here...\n\n# test\nmodel.eval()\nfor scaled_x, scaled_y, x, y, x_date_enc, y_date_enc in dataloader.test_loader:\n ....your test code here...\n```\n\n\n# Dev Install \n\n## install requirements\n> Note:This library assumes that you've installed Pytorch according to it's official website, the basic dependencies of torch > > related libraries may not be listed in the requirements files:\nhttps://pytorch.org/get-started/locally/\n\n**The recommended python version is 3.8.1+.**\n1. fork this project \n\n2. clone this project (latest version)\n```\ngit clone https://github.com/wayne155/pytorch_timeseries\n```\n\n3. install requirements.\n```\npip install -r ./requirements.txt\n```\n\n4. change some code and push to the forked repo\n\n5. create a pull request to this repo\n",
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