# PaddleTS
**Repository Path**: VisionDeveloper/PaddleTS
## Basic Information
- **Project Name**: PaddleTS
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 2
- **Created**: 2022-10-23
- **Last Updated**: 2022-10-23
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[简体中文](./README_cn.md) | **English**
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PaddleTS - PaddlePaddle-based Time Series Modeling in Python
PaddleTS is an easy-to-use Python library for deep time series modeling,
focusing on the state-of-the-art deep neural network models based on
PaddlePaddle deep learning framework. It aims to provide great flexibility
and excellent user experiences for practitioners and professionals. It’s featured with:
* A unified data structure named TSDataset for representing time series data with one
or multiple target variables and optional different kinds of covariates
(e.g. known covariates, observed covariates, static covariates, etc.)
* A base model class named PaddleBaseModelImpl , which inherits from the PaddleBaseModel
and further encapsulates some routine procedures (e.g. data loading, callbacks setup,
loss computation, training loop control, etc.) and allows developers to focus on
the implementation of network architectures when developing new models
* A set of state-of-the-art deep learning models (e.g. NBEATS, NHiTS, LSTNet, TCN, Transformer, etc.)
* A set of transformation operators for data preprocessing (e.g. missing values/outliers handling,
one-hot encoding, normalization, and automatic date/time-related covariate generation, etc.)
* A set of analysis operators for quick data exploration (e.g. basic statistics and summary)
* Automatic hyper-parameter tuning, enhance model performance easily and efficiently
Recently updated:
* Time series representation learning model supported, with TS2Vec model released
* Probabilistic forecasting supported, with DeepAR model released
* Some new time series forecasting models like Informer released
* Auto tuning module AutoTS released, which supports mainstream HPO algorithms and shows significant improvement on multiple models and datasets
* Third-party (e.g. scikit-learn) ML models & data transformations integration supported
In the future, more advanced features will be coming, including:
* Time series anomaly detection
* More time series representation learning models
* More probabilistic forecasting models
* Scenario-specific pipelines which aim to provide an end-to-end solution for solving real-world business problems
* And more
## More About PaddleTS
Specifically, PaddleTS consists of the following modules:
| Module | Description |
|------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------|
| [**paddlets.datasets**](https://paddlets.readthedocs.io/en/latest/source/modules/datasets/overview.html) | Unified time series representation (TSDataset) and data repository with pre-built TSDatasets. |
| [**paddlets.autots**](https://paddlets.readthedocs.io/en/latest/source/modules/autots/overview.html) | Automatic hyper-parameter tuning. |
| [**paddlets.transform**](https://paddlets.readthedocs.io/en/latest/source/modules/transform/overview.html) | Data preprocessing and data transformations. |
| [**paddlets.models**](https://paddlets.readthedocs.io/en/latest/source/modules/models/overview.html) | PaddlePaddle-based deep neural network models for time series modeling. |
| [**paddlets.pipeline**](https://paddlets.readthedocs.io/en/latest/source/modules/pipeline/overview.html) | Pipeline for building time series analysis and modeling workflows. |
| [**paddlets.metrics**](https://paddlets.readthedocs.io/en/latest/source/modules/metrics/overview.html) | Metrics for measuring the performance of a model. |
| [**paddlets.analysis**](https://paddlets.readthedocs.io/en/latest/source/modules/analysis/overview.html) | Quick data exploration and advanced data analysis. |
| [**paddlets.utils**](https://paddlets.readthedocs.io/en/latest/source/modules/backtest/overview.html) | Utility functions. |
## Installation
### Prerequisites
* python >= 3.7
* paddlepaddle >= 2.3
Install paddlets via pip:
```bash
pip install paddlets
```
To get more detailed information, please refer to [Installation](https://paddlets.readthedocs.io/en/latest/source/installation/overview.html).
## Documentation
* [Get Started](https://paddlets.readthedocs.io/en/latest/source/get_started/get_started.html)
* [API Reference](https://paddlets.readthedocs.io/en/latest/source/api/paddlets.analysis.html)
## Community
Feel free to scan below WeChat QR code to join PaddleTS community for technical discussion with PaddleTS maintainers and community members:
## Contributions
We appreciate all kinds of contributions. Please let us know if you encounter any bug by [filing an issue](https://github.com/PaddlePaddle/PaddleTS/issues).
If you are willing to contribute back bug-fixes, please go ahead without any further discussion.
If you plan to contribute new features, utility functions, or extensions to the core, please first open an issue and discuss with us.
Sending a PR without discussion might end up with rejection because we might be taking the core in a different direction than you might be aware of.
## License
PaddleTS has an Apache-style license, as found in the [LICENSE](LICENSE) file.