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Statistical ⚡️ Forecast Lightning fast forecasting with statistical and econometric models
CI Python PyPi conda-nixtla License docs
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 pip 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.
In the guide, we showcase AutoARIMA and AutoETS, and go further into probabilistic predictions, exogenous variables, and other baseline models.
AutoARIMA in Python and R.Fastest and most accurate ETS in Python and R.
Replace FB-Prophet in two lines of code and gain speed and accuracy. Check the experiments here.
Distributed computation in clusters with ray. (Forecast 1M series in 30min)
Good Ol' sklearn interface with AutoARIMA().fit(y).predict(h=7).
exogenous variables and prediction intervals for ARIMA.pmdarima.R.Prophet.NeuralProphet.statsmodels.numba.Out of the box implementation of ADIDA, HistoricAverage, CrostonClassic, CrostonSBA, CrostonOptimized, SeasonalWindowAverage, SeasonalNaive, IMAPA
Naive, RandomWalkWithDrift, WindowAverage, SeasonalExponentialSmoothing, TSB, AutoARIMA and ETS.
Missing something? Please open an issue or write us in Slack
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 AutoARIMA 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](./experiments/arima/).
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 results [here](./experiments/ets/)
With StatsForecast you can fit 9 benchmark models on 1,000,000 series in under 5 min. Reproduce the results [here](./experiments/benchmarks_at_scale/).
You can run this notebooks to get you started.
Example of different AutoARIMA models on M4 data Open In Colab
AutoARIMA.
The AutoARIMA model 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 deploying AutoARIMA in production for a large number of time series.Shorter Example of fitting and AutoARIMA and an ETS model. Open In Colab
Benchmarking 9 models on millions of [series](./experiments/benchmarks_at_scale/).
Here is a link to the documentation.
See CONTRIBUTING.md.
AutoARIMA model is based (translated) from the R implementation included in the forecast package developed by Rob Hyndman.ETS model is based (translated) from the R implementation included in the forecast package developed by Rob Hyndman.Thanks goes to these wonderful people (emoji key):
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This project follows the all-contributors specification. Contributions of any kind welcome!
*Note that all licence references and agreements mentioned in the Statsforecast README section above
are relevant to that project's source code only.
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