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Description:

giottotime 0.2.2

giotto-time
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.
License
giotto-time is distributed under the AGPLv3 license.
If you need a different distribution license, please contact the L2F team at business@l2f.ch.
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
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
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
maintainers@giotto.ai