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https://github.com/tinkoff-ai/etna-ts

ETNA – Time-Series Library
https://github.com/tinkoff-ai/etna-ts
deep-learning forecasting machine-learning python time-series timeseries
Last synced: 3 months ago
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ETNA – Time-Series Library
Host: GitHub
URL: https://github.com/tinkoff-ai/etna-ts
Owner: tinkoff-ai
License: apache-2.0
Archived: true
Created: 2021-08-27T14:02:56.000Z (over 3 years ago)
Default Branch: master
Last Pushed: 2023-08-09T15:52:57.000Z (over 1 year ago)
Last Synced: 2024-05-23T07:47:05.231Z (7 months ago)
Topics: deep-learning, forecasting, machine-learning, python, time-series, timeseries
Language: Python
Homepage: https://etna.tinkoff.ru
Size: 128 MB
Stars: 840
Watchers: 8
Forks: 77
Open Issues: 49
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README

        






Predict your time series the easiest way




  

  

  



 



  

  

  

  





  

  

  

  





  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](https://www.tinkoff.ru/eng/)

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](https://github.com/tinkoff-ai/etna/blob/master/CONTRIBUTING.md).

## Get started

Let's load and prepare the data.

```python

import pandas as pd

from etna.datasets import TSDataset

# 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 = 14

# Make train/test split

train_ts, test_ts = ts.train_test_split(test_size=HORIZON)

```

Define transformations and model:

```python

from etna.models import CatBoostMultiSegmentModel

from etna.transforms import DateFlagsTransform

from etna.transforms import DensityOutliersTransform

from etna.transforms import FourierTransform

from etna.transforms import LagTransform

from etna.transforms import LinearTrendTransform

from etna.transforms import MeanTransform

from etna.transforms import SegmentEncoderTransform

from etna.transforms import TimeSeriesImputerTransform

from etna.transforms import TrendTransform

# Prepare transforms

transforms = [

    DensityOutliersTransform(in_column="target", distance_coef=3.0),

    TimeSeriesImputerTransform(in_column="target", strategy="forward_fill"),

    LinearTrendTransform(in_column="target"),

    TrendTransform(in_column="target", out_column="trend"),

    LagTransform(in_column="target", lags=list(range(HORIZON, 122)), out_column="target_lag"),

    DateFlagsTransform(week_number_in_month=True, out_column="date_flag"),

    FourierTransform(period=360.25, order=6, out_column="fourier"),

    SegmentEncoderTransform(),

    MeanTransform(in_column=f"target_lag_{HORIZON}", window=12, seasonality=7),

    MeanTransform(in_column=f"target_lag_{HORIZON}", window=7),

]

# Prepare model

model = CatBoostMultiSegmentModel()

```

Fit `Pipeline` and make a prediction:

```python

from etna.pipeline import Pipeline

# Create and fit the pipeline

pipeline = Pipeline(model=model, transforms=transforms, horizon=HORIZON)

pipeline.fit(train_ts)

# Make a forecast

forecast_ts = pipeline.forecast()

```

Let's plot the results:

```python

from etna.analysis import plot_forecast

plot_forecast(forecast_ts=forecast_ts, test_ts=test_ts, train_ts=train_ts, n_train_samples=50)

```

![](examples/assets/readme/get_started.png)

Print the metric value across the segments:

```python

from etna.metrics import SMAPE

metric = SMAPE(mode="macro")

metric_value = metric(y_true=test_ts, y_pred=forecast_ts)

>>> {'segment_b': 3.3017151519000967, 'segment_c': 5.270557433427279, 'segment_a': 5.272811627335398, 'segment_d': 4.689085450895735}

```

## Installation 

ETNA is available on [PyPI](https://pypi.org/project/etna), so you can use `pip` to install it.

Install default version:

```bash

pip install --upgrade pip

pip install etna

```

The default version doesn't contain all the dependencies, because some of them are needed only for specific models, e.g. Prophet, PyTorch.

Available user extensions are the following:

* `prophet`: adds prophet model`,

* `torch`: adds models based on neural nets,

* `wandb`: adds wandb logger,

* `auto`: adds AutoML functionality,

* `classiciation`: adds time series classification functionality.

Install extension:

```bash

pip install etna[extension-name]

```

Install all extensions:

```bash

pip install etna[all]

```

There are also developer extensions. All the extensions are listed in [`pyproject.toml`](https://github.com/tinkoff-ai/etna/blob/master/pyproject.toml#L93).

Without the appropriate extension you will get an `ImportError` trying to import the model that needs it.

For example, `etna.models.ProphetModel` needs `prophet` extension and can't be used without it.

### Configuration

ETNA supports configuration files. It means that library will check that all the specified packages are installed prior to script start and NOT during runtime. 

To set up a configuration for your project you should create a `.etna` file at the project's root. To see the available options look at [`Settings`](https://github.com/tinkoff-ai/etna/blob/master/etna/settings.py#L68). There is an [example](https://github.com/tinkoff-ai/etna/tree/master/examples/configs/.etna) of configuration file.

## Tutorials

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

| Notebook     | Interactive launch  |

|:----------|------:|

| [Get started](https://github.com/tinkoff-ai/etna/tree/master/examples/get_started.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/get_started.ipynb) |

| [Backtest](https://github.com/tinkoff-ai/etna/tree/master/examples/backtest.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/backtest.ipynb) |

| [EDA](https://github.com/tinkoff-ai/etna/tree/master/examples/EDA.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/EDA.ipynb) |

| [Regressors and exogenous data](https://github.com/tinkoff-ai/etna/tree/master/examples/exogenous_data.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/exogenous_data.ipynb) |

| [Custom model and transform](https://github.com/tinkoff-ai/etna/tree/master/examples/custom_transform_and_model.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/custom_transform_and_model.ipynb) |

| [Deep learning models](https://github.com/tinkoff-ai/etna/tree/master/examples/NN_examples.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/NN_examples.ipynb) |

| [Ensembles](https://github.com/tinkoff-ai/etna/tree/master/examples/ensembles.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/ensembles.ipynb) |

| [Outliers](https://github.com/tinkoff-ai/etna/tree/master/examples/outliers.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/outliers.ipynb) |

| [Forecasting strategies](https://github.com/tinkoff-ai/etna/tree/master/examples/forecasting_strategies.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/forecasting_strategies.ipynb) |

| [Forecast interpretation](https://github.com/tinkoff-ai/etna/tree/master/examples/forecast_interpretation.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/forecast_interpretation.ipynb) |

| [Clustering](https://github.com/tinkoff-ai/etna/tree/master/examples/clustering.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/clustering.ipynb) |

| [AutoML](https://github.com/tinkoff-ai/etna/tree/master/examples/automl.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/automl.ipynb) |

| [Inference: using saved pipeline on a new data](https://github.com/tinkoff-ai/etna/tree/master/examples/inference.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/inference.ipynb) |

| [Hierarchical time series](https://github.com/tinkoff-ai/etna/blob/master/examples/hierarchical_pipeline.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/hierarchical_pipeline.ipynb) |

| [Classification](https://github.com/tinkoff-ai/etna/blob/master/examples/classification.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/classification.ipynb) |

| [Feature selection](https://github.com/tinkoff-ai/etna/blob/master/examples/feature_selection.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/feature_selection.ipynb) |

## Documentation

ETNA documentation is available [here](https://etna-docs.netlify.app/).

## Community

To ask the questions or discuss the library you can join our [telegram chat](https://t.me/etna_support). 

[Discussions section](https://github.com/tinkoff-ai/etna/discussions) on github is also open for this purpose.

## Resources

- [Forecasting with ETNA: Fast and Furious](https://medium.com/its-tinkoff/forecasting-with-etna-fast-and-furious-1b58e1453809) on Medium

- [ETNA Regressors](https://medium.com/its-tinkoff/etna-regressors-d2722923e88e) on Medium

- [Time series forecasting with ETNA: first steps](https://medium.com/its-tinkoff/time-series-forecasting-with-etna-first-steps-dfaf90c5b919) on Medium

- [ETNA: Time Series Analysis. What, why and how?](https://medium.com/its-tinkoff/etna-time-series-analysis-what-why-and-how-e45557af4f6c) on Medium

- [Time Series Forecasting Strategies in ETNA](https://medium.com/its-tinkoff/time-series-forecasting-strategies-in-etna-93d7d2f8a911) on Medium

- [Tabular Playground Series - Mar 2022 (7th place!)](https://www.kaggle.com/code/chikovalexander/tps-mar-2022-etna/notebook?scriptVersionId=91575908) on Kaggle

- [Store sales prediction with etna library](https://www.kaggle.com/dmitrybunin/store-sales-prediction-with-etna-library?scriptVersionId=81104235) on Kaggle

- [Tabular Playground Series - Jan 2022](https://www.kaggle.com/code/chikovalexander/tps-jan-2022-etna/notebook) on Kaggle

- [EDA notebook for Ubiquant Market Prediction](https://www.kaggle.com/code/martins0n/ubiquant-eda-toy-predictions-etna) on Kaggle

- [PyCon Russia September 2021 talk](https://youtu.be/VxWHLEFgXnE) on YouTube

- [ETNA Meetup Jun 2022](https://www.youtube.com/watch?v=N1Xy3EqY058&list=PLLrf_044z4JrSsjMd-3dF6VbBLPI_yOxG) on YouTube

- [DUMP May 2022 talk](https://youtu.be/12uuxepdtks) on YouTube

- [Прикладные задачи анализа данных, лекция 8 — Временные ряды 2](https://youtu.be/1gXVbidDZck) on YouTube

 

## Acknowledgments

### ETNA.Team

[Andrey Alekseev](https://github.com/iKintosh),

[Nikita Barinov](https://github.com/diadorer),

[Dmitriy Bunin](https://github.com/Mr-Geekman),

[Aleksandr Chikov](https://github.com/alex-hse-repository),

[Vladislav Denisov](https://github.com/v-v-denisov),

[Martin Gabdushev](https://github.com/martins0n),

[Sergey Kolesnikov](https://github.com/Scitator),

[Artem Makhin](https://github.com/Ama16),

[Ivan Mitskovets](https://github.com/imitskovets),

[Albina Munirova](https://github.com/albinamunirova),

[Julia Shenshina](https://github.com/julia-shenshina),

[Yuriy Tarasyuk](https://github.com/DBcreator),

[Konstantin Vedernikov](https://github.com/scanhex12),

[Ivan Nedosekov](https://github.com/GrozniyToaster),

[Rodion Petrov](https://github.com/Noidor1)

### ETNA.Contributors

[WinstonDovlatov](https://github.com/WinstonDovlatov),

[mvakhmenin](https://github.com/mvakhmenin),

[Carlosbogo](https://github.com/Carlosbogo),

[Pacman1984](https://github.com/Pacman1984),

[looopka](https://github.com/looopka),

[Artem Levashov](https://github.com/soft1q),

[Aleksey Podkidyshev](https://github.com/alekseyen)

## License

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

ETNA is covered by [Apache 2.0](/LICENSE). 

Read more about this license [here](https://choosealicense.com/licenses/apache-2.0/)

> Please note that `etna[prophet]` is covered by [GPL 2.0](https://www.gnu.org/licenses/old-licenses/gpl-2.0.html) due to pystan package.