ETNA Time Series Library
Predict your time series the easiest way
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.
ETNA is on PyPI, so you can use
pip to install it.
pip install --upgrade pip pip install etna
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()
We have also prepared a set of tutorials for an easy introduction:
|Deep learning models|
ETNA documentation is available here.
Andrey Alekseev, Nikita Barinov, Dmitriy Bunin, Aleksandr Chikov, Vladislav Denisov, Martin Gabdushev, Sergey Kolesnikov, Artem Makhin, Ivan Mitskovets, Albina Munirova, Nikolay Romantsov, Julia Shenshina
Feel free to use our library in your commercial and private applications.
ETNA is covered by . Read more about this license here