ETNA Time Series Library

Predict your time series the easiest way

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ETNA is an easy-to-use time series forecasting framework. It includes built in toolkits for time series preprocessing, feature generation, a variety of predictive models with unified interface - from classic machine learning to SOTA neural networks, models combination methods and smart backtesting. ETNA is designed to make working with time series simple, productive, and fun.

ETNA is the first python open source framework of Tinkoff.ru Artificial Intelligence Center. The library started as an internal product in our company - we use it in over 10+ projects now, so we often release updates. Contributions are welcome - check our Contribution Guide.

Installation

ETNA is on PyPI, so you can use pip to install it.

pip install --upgrade pip
pip install etna

Get started

Here's some example code for a quick start.

import pandas as pd
from etna.datasets.tsdataset import TSDataset
from etna.models import ProphetModel
from etna.pipeline import Pipeline

# Read the data
df = pd.read_csv("examples/data/example_dataset.csv")

# Create a TSDataset
df = TSDataset.to_dataset(df)
ts = TSDataset(df, freq="D")

# Choose a horizon
HORIZON = 8

# Fit the pipeline
pipeline = Pipeline(model=ProphetModel(), horizon=HORIZON)
pipeline.fit(ts)

# Make the forecast
forecast_ts = pipeline.forecast()

Tutorials

We have also prepared a set of tutorials for an easy introduction:

Documentation

ETNA documentation is available here.

Resources

Acknowledgments

ETNA.Team

Andrey Alekseev, Nikita Barinov, Dmitriy Bunin, Aleksandr Chikov, Vladislav Denisov, Martin Gabdushev, Sergey Kolesnikov, Artem Makhin, Ivan Mitskovets, Albina Munirova, Nikolay Romantsov, Julia Shenshina

ETNA.Contributors

Artem Levashov, Aleksey Podkidyshev, Carlosbogo

License

Feel free to use our library in your commercial and private applications.

ETNA is covered by Apache 2.0. Read more about this license here