# Time-series-prediction

**Repository Path**: zeroonekevin/Time-series-prediction

## Basic Information

- **Project Name**: Time-series-prediction
- **Description**: A collection of time series prediction methods: rnn, seq2seq, cnn, wavenet, transformer, unet, n-beats, gan, kalman-filter
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-01-26
- **Last Updated**: 2021-01-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Time series prediction
[![LICENSE](https://img.shields.io/badge/license-Anti%20996-blue.svg)](https://github.com/996icu/996.ICU/blob/master/LICENSE)<br>
This repository implements the common methods of time series prediction, especially deep learning methods in TensorFlow2. 
It's welcomed to contribute if you have any better idea, just create a PR. If any question, feel free to open an issue.

#### Ongoing project, I will continue to improve this, so you might want to watch/star this repo to revisit.

<table style="width:100%" align="center">  
  <tr>
    <th>
      <p align="center">
      RNN                  
      </p>
    </th>
    <th>
      <p align="center">
           <a href="./docs/rnn.md" name="introduction">intro</a>      
      </p>
    </th>
    <th>
      <p align="center">
           <a href="./deepts/models/seq2seq.py" name="code">code</a>     
      </p>
    </th>
  </tr>
  <tr>
    <th>
      <p align="center">
      wavenet                 
      </p>
    </th>
    <th>
      <p align="center">
           <a href="./docs/wavenet.md" name="introduction">intro</a>      
      </p>
    </th>
    <th>
      <p align="center">
           <a href="./deepts/models/wavenet.py" name="code">code</a>     
      </p>
    </th>
  </tr>
  <tr>
    <th>
      <p align="center">
      transformer           
      </p>
    </th>
    <th>
      <p align="center">
           <a href="./docs/transformer.md" name="introduction">intro</a>              
      </p>   
    </th>
    <th> 
      <p align="center">
           <a href="./deepts/models/transformer.py" name="code">code</a>     
      </p>      
    </th>
  </tr>
  <tr>
    <th>
      <p align="center">
      U-Net                  
      </p>
    </th>
    <th>
      <p align="center">
           <a href="./docs/unet.md" name="introduction">intro</a>     
      </p>
    </th>
    <th>
      <p align="center">
           <a href="./deepts/models/unet.py" name="code">code</a>     
      </p>      
    </th>
  </tr>
  <tr>
    <th>
      <p align="center">
      n-beats                  
      </p>
    </th>
    <th>
      <p align="center">
            <a href="./docs/nbeats.md" name="introduction">intro</a>     
      </p>
    </th>
    <th>
      <p align="center">
           <a href="./deepts/models/nbeats.py" name="code">code</a>     
      </p>      
    </th>
  </tr>
  <tr>
    <th>
      <p align="center">
      GAN                   
      </p>
    </th>
    <th>
      <p align="center">
           <a href="./docs/gan.md" name="introduction">intro</a>      
      </p>
    </th>
    <th>
      <p align="center">
           <a href="./deepts/models/gan.py" name="code">code</a>     
      </p>      
    </th>
  </tr>
</table>


## Usage
1. Install the required library
```bash
$ pip install -r requirements.txt
```
2. Download the data, if necessary
```bash
$ bash ./data/download_passenger.sh
```
3. Train the model <br>
set `custom_model_params` if you want (refer to params in `./deepts/models/*.py`), and pay attention to feature engineering.

```bash
$ cd examples
$ python run_train.py --use_model seq2seq
$ cd ..
$ tensorboard --logdir=./data/logs
```
4. Predict new data
```bash
$ cd examples
$ python run_test.py
```

## Further reading
- https://github.com/awslabs/gluon-ts/
- https://github.com/Azure/DeepLearningForTimeSeriesForecasting
- https://github.com/microsoft/forecasting