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https://github.com/benman1/python-time-series
Time-Series analysis, statistical and machine learning models for forecasting, regression, and classification
https://github.com/benman1/python-time-series
darts deep-learning forecasting mlforecast nixtla scikit-learn statsforecast time-series time-series-analysis
Last synced: 1 day ago
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Time-Series analysis, statistical and machine learning models for forecasting, regression, and classification
- Host: GitHub
- URL: https://github.com/benman1/python-time-series
- Owner: benman1
- License: mit
- Created: 2022-12-23T10:27:44.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-12-02T14:29:18.000Z (2 months ago)
- Last Synced: 2024-12-02T15:24:10.271Z (2 months ago)
- Topics: darts, deep-learning, forecasting, mlforecast, nixtla, scikit-learn, statsforecast, time-series, time-series-analysis
- Language: Jupyter Notebook
- Homepage: https://amzn.to/4iiFWWa
- Size: 11.3 MB
- Stars: 1
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
This is the code repository for Machine Learning for Time Series, published by Packt.
# python-time-series
Time-Series analysis, statistical and machine learning models for forecasting, regression, and classification
## Environment
You can install your local environment with conda (recommended) or pip. The environment configurations for conda and pip are provided. Please note that if you choose pip as you installation tool, you might need additional tweaking.
If you have any problems with the environment, please raise an issue, where you show the error you got. If you feel confident, please go ahead and create a pull request.
### Conda
This is the recommended method for installing dependencies. Please make sure you have [anaconda](https://www.anaconda.com/download) installed.
First create the environment for the book that contains all the dependencies:
```bash
conda env create --file time_series.yml
```
The conda environment is called `time_series`. You can activate it as follows:
```bash
conda activate time_series
```
### Pip
[Pip](https://pypi.org/project/pip/) is the default dependency management tool in Python. With pip, you should be able to install all the libraries from the requirements file:
```bash
pip install -r requirements.txt
```
### Docker
There's a [docker](https://www.docker.com/) file for the environment as well. It uses the docker environment and starts an ipython notebook. To use it, first build it, and then run it:
```bash
docker build -t new_image .
docker run -it new_image
```
You should be able to find the notebook in your browser at [http://localhost:8080](http://localhost:8080).
### Poetry
Make sure you have [poetry](https://python-poetry.org/) installed. On Linux and MacOS, you should be able to use the requirements file:
```bash
poetry init
cat requirements.txt | xargs poetry add
```
## Contributing
If you find anything amiss with the notebooks or dependencies, please feel free to create a pull request.
If you want to change the conda dependency specification (the yaml file), you can test it like this:
```bash
conda env create --file time_series.yml --force
```
You can update the pip requirements like this:
```bash
pip freeze > requirements.txt
```
Please make sure that you keep these two ways of maintaining dependencies in sync.
Then make sure, you test the notebooks in the new environment to see that they run.