StatsForecast offers a collection of widely used univariate time series forecasting models, including automatic ARIMA and ETS modeling optimized for high performance using numba. It also includes a large battery of benchmarking models.
PyPI
You can install the released version of StatsForecast from the Python package index with:
pip install statsforecast(Installing inside a python virtualenvironment or a conda environment is recommended.)
Conda
Also you can install the released version of StatsForecast from conda with:
conda install -c conda-forge statsforecast(Installing inside a python virtualenvironment or a conda environment is recommended.)
Dev Mode
If you want to make some modifications to the code and see the effects in real time (without reinstalling), follow the steps below:git clone https://github.com/Nixtla/statsforecast.git
cd statsforecast
pip install -e .To get started just follow this guide.
ETS Example: 4x faster than StatsModels with improved accuracy and robustness.
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Fastest and most accurate
auto_arimainPythonandR. -
Fastest and most accurate
etsinPythonandR. -
Replace FB-Prophet in two lines of code and gain speed and accuracy. Check the experiments here.
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Distributed computation in clusters with ray. (Forecast 1M series in 30min)
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Good Ol' sklearn interface with
AutoARIMA().fit(y).predict(h=7).
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Inclusion of
exogenous variablesandprediction intervalsfor ARIMA. -
20x faster than
pmdarima. -
1.5x faster than
R. -
500x faster than
Prophet. -
4x faster than
statsmodels. -
Compiled to high performance machine code through
numba. -
Out of the box implementation of
ses,adida,historic_average,croston_classic,croston_sba,croston_optimized,seasonal_window_average,seasonal_naive,imapanaive,random_walk_with_drift,window_average,seasonal_exponential_smoothing,tsb,auto_arimaandets.
Missing something? Please open an issue or write us in
Current Python alternatives for statistical models are slow, inaccurate and don't scale well. So we created a library that can be used to forecast in production environments or as benchmarks. StatsForecast includes an extensive battery of models that can efficiently fit millions of time series.
The auto_arima model implemented in StatsForecast is 20x faster than pmdarima and 1.5x faster than R while improving accuracy. You can see the exact comparison and reproduce the results here.
StatsForecast's exponential smoothing is 4x faster than StatsModels' and 1.6x faster than R's, with improved accuracy and robustness. You can see the exact comparison and reproduce the resultshere
With StatsForecast you can fit 9 benchmark models on 1,000,000 series in under 5 min. Reproduce the results here.
You can run this notebooks to get you started.
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Example of different
auto_arimamodels on M4 data- In this notebook we present Nixtla's
auto_arima. Theauto_arimamodel is widely used to forecast time series in production and as a benchmark. However, the alternative python implementation (pmdarima) is so slow that prevents data scientists from quickly iterating and deployingauto_arimain production for a large number of time series.
- In this notebook we present Nixtla's
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Shorter Example of fitting and
auto_arimaand anetsmodel. -
Benchmarking 9 models on millions of series.
Here is a link to the documentation.
See CONTRIBUTING.md.
- The
auto_arimamodel is based (translated) from the R implementation included in the forecast package developed by Rob Hyndman. - The
etsmodel is based (translated) from the R implementation included in the forecast package developed by Rob Hyndman.
Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specification. Contributions of any kind welcome!