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https://github.com/giotto-ai/giotto-time
Time series analysis suite
https://github.com/giotto-ai/giotto-time
Last synced: 3 days ago
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Time series analysis suite
- Host: GitHub
- URL: https://github.com/giotto-ai/giotto-time
- Owner: giotto-ai
- License: other
- Created: 2019-11-06T09:20:57.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-10-28T08:32:27.000Z (about 2 years ago)
- Last Synced: 2025-01-02T10:09:09.529Z (10 days ago)
- Language: Python
- Size: 7.14 MB
- Stars: 98
- Watchers: 6
- Forks: 24
- Open Issues: 19
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.rst
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.rst
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# giotto-time
giotto-time is a machine learning based time series forecasting toolbox in Python.
It is part of the [Giotto](https://github.com/giotto-ai) collection of open-source projects and aims to provide
feature extraction, analysis, causality testing and forecasting models based on
[scikit-learn](https://scikit-learn.org/stable/) API.
## License
giotto-time is distributed under the AGPLv3 [license](https://github.com/giotto-ai/giotto-time/blob/master/LICENSE).
If you need a different distribution license, please contact the L2F team at [email protected].
## Documentation
- API reference (stable release): https://giotto-ai.github.io/giotto-time/
## Getting started
Get started with giotto-time by following the installation steps below.
Simple tutorials and real-world use cases can be found in example folder as notebooks.
## Installation
### User installation
Run this command in your favourite python environment
```
pip install giotto-time
```
### Developer installation
Get the latest state of the source code with the command
```
git clone https://github.com/giotto-ai/giotto-time.git
cd giotto-time
pip install -e ".[tests, doc]"
```
## Example
```python
from gtime import *
from gtime.feature_extraction import *
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
# Create random DataFrame with DatetimeIndex
X_dt = pd.DataFrame(np.random.randint(4, size=(20)),
index=pd.date_range("2019-12-20", "2020-01-08"),
columns=['time_series'])
# Convert the DatetimeIndex to PeriodIndex and create y matrix
X = preprocessing.TimeSeriesPreparation().transform(X_dt)
y = model_selection.horizon_shift(X, horizon=2)
# Create some features
cal = feature_generation.Calendar(region="europe", country="Switzerland", kernel=np.array([1, 2]))
X_f = compose.FeatureCreation(
[('s_2', Shift(2), ['time_series']),
('ma_3', MovingAverage(window_size=3), ['time_series']),
('cal', cal, ['time_series'])]).fit_transform(X)
# Train/test split
X_train, y_train, X_test, y_test = model_selection.FeatureSplitter().transform(X_f, y)
# Try sklearn's MultiOutputRegressor as time-series forecasting model
gar = forecasting.GAR(LinearRegression())
gar.fit(X_train, y_train).predict(X_test)
```
## Contributing
We welcome new contributors of all experience levels. The Giotto
community goals are to be helpful, welcoming, and effective. To learn more about
making a contribution to giotto-time, please see the [CONTRIBUTING.rst](https://github.com/giotto-ai/giotto-time/blob/master/CONTRIBUTING.rst)
file.
## Links
- Official source code repo: https://github.com/giotto-ai/giotto-time
- Download releases: https://pypi.org/project/giotto-time/
- Issue tracker: https://github.com/giotto-ai/giotto-time/issues
## Community
Giotto Slack workspace: https://slack.giotto.ai/
## Contacts