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.
This project has been quarantined.
PyPI Admins need to review this project before it can be restored. While in quarantine, the project is not installable by clients, and cannot be being modified by its maintainers.
Read more in the project in quarantine help article.
Project description
ETNA Time Series Library
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("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 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:
| Notebook | Interactive launch |
|---|---|
| Get started | |
| Backtest | |
| EDA | |
| Outliers | |
| Clustering | |
| Deep learning models | |
| Ensembles |
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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file etna-ts-1.3.1.tar.gz.
File metadata
- Download URL: etna-ts-1.3.1.tar.gz
- Upload date:
- Size: 98.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.1.11 CPython/3.9.8 Linux/5.11.0-1020-azure
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
de85e45cae0a685f5be30ad85650972fc84562a67f323fe675e8512ebd6786de
|
|
| MD5 |
bfa3839aec5394021368b868d12f4c2e
|
|
| BLAKE2b-256 |
a7beb7e2e3cbc2e7dd0603c42aa4a1be3e4852313eef86abf2c1c4eb3d71c408
|
File details
Details for the file etna_ts-1.3.1-py3-none-any.whl.
File metadata
- Download URL: etna_ts-1.3.1-py3-none-any.whl
- Upload date:
- Size: 138.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.1.11 CPython/3.9.8 Linux/5.11.0-1020-azure
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
18fff774496f3e437d5530124209a4ab48424d1a0b44c2fa0cf5fad105a5cd7e
|
|
| MD5 |
d73bac1f132a0bb7f2eac940887b760f
|
|
| BLAKE2b-256 |
9a8c6049a7e9d09290dfa78377c0d9aaae565d1a3b8c469495b5863719267ba9
|