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ETNA is the first python open source framework of Tinkoff.ru AI Center. It is designed to make working with time series simple, productive, and fun.

Project description

ETNA Time Series Library

Pipi version PyPI Status

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Homepage | Documentation | Tutorials | Contribution Guide | Release Notes

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-ts

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

# Read the data
df = pd.read_csv("example_dataset.csv")
df["timestamp"] = pd.to_datetime(df["timestamp"])

# Create a TSDataset
df = TSDataset.to_dataset(df)
ts = TSDataset(df,freq='1d')

# Choose a horizon
HORIZON = 8

# Fit the model
model = ProphetModel()
model.fit(ts)

# Make the forecast
future_ts = ts.make_future(HORIZON)
forecast_ts = model.forecast(future_ts)

Tutorials

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

01. Get started

  • Creating TSDataset and time series plotting
  • Forecast single time series - Simple forecast, Prophet, Catboost
  • Forecast multiple time series

02. Backtest

  • What is backtest and how it works
  • How to run a validation
  • Validation visualisation

03. EDA

  • Visualization
    • Plot
    • Partial autocorrelation
    • Cross-correlation
    • Distribution
  • Outliers
    • Median method
    • Density method

Documentation

ETNA documentation is available here.

Acknowledgments

ETNA.Team

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

ETNA.Contributors

Levashov Artem, Podkidyshev Aleksey

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

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