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.

Get started

Let's load and prepare the data. ```python import pandas as pd from etna.datasets import TSDataset

Read the data

df = pd.readcsv("examples/data/exampledataset.csv")

Create a TSDataset

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

Choose a horizon

HORIZON = 14

Make train/test split

traints, testts = ts.traintestsplit(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(incolumn="target", distancecoef=3.0), TimeSeriesImputerTransform(incolumn="target", strategy="forwardfill"), LinearTrendTransform(incolumn="target"), TrendTransform(incolumn="target", outcolumn="trend"), LagTransform(incolumn="target", lags=list(range(HORIZON, 122)), outcolumn="targetlag"), DateFlagsTransform(weeknumberinmonth=True, outcolumn="dateflag"), FourierTransform(period=360.25, order=6, outcolumn="fourier"), SegmentEncoderTransform(), MeanTransform(incolumn=f"targetlag{HORIZON}", window=12, seasonality=7), MeanTransform(incolumn=f"targetlag{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

plotforecast(forecastts=forecastts, testts=testts, traints=traints, ntrain_samples=50) ```

Print the metric value across the segments: ```python from etna.metrics import SMAPE

metric = SMAPE(mode="macro") metricvalue = metric(ytrue=testts, ypred=forecast_ts)

{'segmentb': 3.3017151519000967, 'segmentc': 5.270557433427279, 'segmenta': 5.272811627335398, 'segmentd': 4.689085450895735} ```

Installation

ETNA is available on PyPI, 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.

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. There is an example of configuration file.

Tutorials

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

| Notebook | Interactive launch | |:----------|------:| | Get started | | | Backtest | | | EDA | | | Regressors and exogenous data | | | Custom model and transform | | | Deep learning models | | | Ensembles | | | Outliers | | | Forecasting strategies | | | Forecast interpretation | | | Clustering | | | AutoML | | | Inference: using saved pipeline on a new data | | | Hierarchical time series | | | Classification | | | Feature selection | |

Documentation

ETNA documentation is available here.

Community

To ask the questions or discuss the library you can join our telegram chat. Discussions section on github is also open for this purpose.

Resources

• Forecasting with ETNA: Fast and Furious on Medium

• ETNA Regressors on Medium

• Time series forecasting with ETNA: first steps on Medium

• ETNA: Time Series Analysis. What, why and how? on Medium

• Time Series Forecasting Strategies in ETNA on Medium

• Tabular Playground Series - Mar 2022 (7th place!) on Kaggle

• Store sales prediction with etna library on Kaggle

• Tabular Playground Series - Jan 2022 on Kaggle

• EDA notebook for Ubiquant Market Prediction on Kaggle

• PyCon Russia September 2021 talk on YouTube

• ETNA Meetup Jun 2022 on YouTube

• DUMP May 2022 talk on YouTube

• Прикладные задачи анализа данных, лекция 8 — Временные ряды 2 on YouTube

Acknowledgments

ETNA.Team

Andrey Alekseev, Nikita Barinov, Dmitriy Bunin, Aleksandr Chikov, Vladislav Denisov, Martin Gabdushev, Sergey Kolesnikov, Artem Makhin, Ivan Mitskovets, Albina Munirova, Julia Shenshina, Yuriy Tarasyuk, Konstantin Vedernikov, Ivan Nedosekov, Rodion Petrov

ETNA.Contributors

WinstonDovlatov, mvakhmenin, Carlosbogo, Pacman1984, looopka, Artem Levashov, Aleksey Podkidyshev

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

Please note that etna[prophet] is covered by GPL 2.0 due to pystan package.