u8darts 0.1.0

A python library for easy manipulation and forecasting time series.

darts: Easy manipulation and forecasting of time series

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darts is a python library for easy manipulation and forecasting time series. It contains a variety of models, from classics such as ARIMA to neural networks. The models can all be used in the same way, using fit() and predict() functions, similar to scikit-learn. The library also makes it easy to backtest models, and combine the predictions of several models and external regressors.

Install

Preconditions

Our direct dependencies include fbprophet and torch which have non-Python dependencies. A Conda environment is thus recommended because it will handle all of those in one go.

The following steps assume running inside a conda environment. If that's not possible, first follow the official instructions to install fbprophet and torch, then skip to Install darts

To create a conda environment for Python 3.7 (after installing conda):

conda create --name <env-name> python=3.7

Don't forget to activate your virtual environment

conda activate <env-name>

MAC

conda install -c conda-forge -c pytorch pip fbprophet pytorch

Linux and Windows

conda install -c conda-forge -c pytorch pip fbprophet pytorch cpuonly

Install darts

pip install u8darts

Running the examples only, without installing:

If the conda setup is causing too many problems, we also provide a Docker image with everything set up for you and ready-to-use python notebooks with demo examples. To run the example notebooks without installing our libraries natively on your machine, you can use our Docker image:

cd scripts
./build_docker.sh && ./run_docker.sh

Then copy and paste the URL provided by the docker container into your browser to access Jupyter notebook.

For this setup to work you need to have a Docker service installed. You can get it at Docker website.

Example Usage

Create TimeSeries object from a Pandas DataFrame, and split in train/validation series:

from darts import TimeSeries

df = pd.read_csv('AirPassengers.csv', delimiter=",")
series = TimeSeries.from_dataframe(df, 'Month', '#Passengers')
train, val = series.split_after(pd.Timestamp('19590101'))

Fit an exponential smoothing model, and make a prediction over the validation series' duration:

from darts import ExponentialSmoothing

model = ExponentialSmoothing()
model.fit(train)
prediction = model.predict(len(val))

Plot:

series.plot(label='actual', lw=3)
prediction.plot(label='forecast', lw=3)
plt.legend()
plt.xlabel('Year')

{ width=100% }

We invite you to go over the example notebooks in the examples directory.

Documentation

The documentation of the API and models is available here.

Features

Currently, the library contains the following features:

Forecasting Models:

• Exponential smoothing,
• ARIMA & auto-ARIMA,
• Facebook Prophet,
• Theta method,
• FFT (Fast Fourier Transform),
• Recurrent neural networks (vanilla RNNs, GRU, and LSTM variants),
• Temporal convolutional network.

Preprocessing: Transformer tool for easily scaling / normalizing time series.

Metrics: A variety of metrics for evaluating time series' goodness of fit; from R2-scores to Mean Absolute Scaled Error.

Backtesting: Utilities for simulating historical forecasts, using moving time windows.

Regressive Models: Possibility to predict a time series from several other time series (e.g., external regressors), using arbitrary regressive models.

Contribute

The development is ongoing, and there are many new features that we want to add. We welcome pull requests and issues on github.