Toolbox for Time Series analysis and integration with Machine Learning.
giotto-time is a machine learning based time series forecasting toolbox in Python. It is part of the Giotto collection of open-source projects and aims to provide feature extraction, analysis, causality testing and forecasting models based on scikit-learn API.
- API reference (stable release): https://docs-time.giotto.ai
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.
Run this command in your favourite python environment
pip install giotto-time
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]"
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)
See the RELEASE.rst file for a history of notable changes to giotto-time.
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 file.
Giotto Slack workspace: https://slack.giotto.ai/
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size giotto_time-0.2.1-py3-none-any.whl (83.5 kB)||File type Wheel||Python version py3||Upload date||Hashes View|
|Filename, size giotto-time-0.2.1.tar.gz (62.0 kB)||File type Source||Python version None||Upload date||Hashes View|
Hashes for giotto_time-0.2.1-py3-none-any.whl