Statsforecast

Lightning ⚡️ fast forecasting with statistical and econometric models.

Programming language: Python

License: Apache License 2.0

Tags: Science And Data Analysis     Machine Learning     Forecasting     Timeseries Data    

README

Nixtla    

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Statistical ⚡️ Forecast Lightning fast forecasting with statistical and econometric models

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.

💻 Installation

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 .

🏃🏻‍♀️🏃 Getting Started

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.

🎉 New!

• ETS Example: 4x faster than StatsModels with improved accuracy and robustness.
• Complete pipeline and comparison: 20x faster than pmdarima and 500x faster than Prophet.

🔥 Highlights

• Fastest and most accurate 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).

🎊 Features

• Inclusion of exogenous variables and prediction intervals for ARIMA.
• 20x faster than pmdarima.
• 1.5x faster than R.
• 500x faster than Prophet.
• 100x faster than NeuralProphet.
• 4x faster than statsmodels.
• Compiled to high performance machine code through numba.

• 1,000,000 series in 30 min with ray.

• 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

📖 Why?

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.

🔬 Accuracy & ⏲ Speed

ARIMA

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/).

ETS

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/)

Benchmarks at Scale

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/).

🧬 Getting Started

You can run this notebooks to get you started.

• Example of different AutoARIMA models on M4 data

  • In this notebook we present Nixtla's 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.

• Benchmarking 9 models on millions of [series](./experiments/benchmarks_at_scale/).

📖 Documentation (WIP)

Here is a link to the documentation.

🔨 How to contribute

See CONTRIBUTING.md.

📃 References

• The AutoARIMA model is based (translated) from the R implementation included in the forecast package developed by Rob Hyndman.
• The ETS model is based (translated) from the R implementation included in the forecast package developed by Rob Hyndman.

Contributors ✨

Thanks goes to these wonderful people (emoji key):

<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section --> <!-- prettier-ignore-start --> <!-- markdownlint-disable --> fede💻 🚧 José Morales💻 🚧 Sugato Ray💻 Jeff Tackes🐛 darinkist🤔 Alec Helyar💬 Dave Hirschfeld💬 mergenthaler💻 Kin💻 Yasslight90🤔 asinig🤔 Philip Gillißen💻 Sebastian Hagn🐛 Han Wang💻 Ben Jeffrey🐛 Beliavsky📖 Mariana Menchero García 💻 Nikhil Gupta🐛 JD🐛 josh attenberg💻 JeroenPeterBos💻

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This project follows the all-contributors specification. Contributions of any kind welcome!