Nixtla

Statistical ⚡️ Forecast

Lightning fast forecasting with statistical and econometric models

[](https://github.com/Nixtla/statsforecast/actions/workflows/ci.yaml) [](https://pypi.org/project/statsforecast/) [](https://pypi.org/project/statsforecast/) [](https://anaconda.org/conda-forge/statsforecast) [](https://github.com/Nixtla/statsforecast/blob/main/LICENSE) [](https://github.com/Nixtla/statsforecast/actions/workflows/build-docs.yaml) [](https://pepy.tech/project/statsforecast) **StatsForecast** offers a collection of widely used univariate time series forecasting models, including automatic `ARIMA`, `ETS`, `CES`, and `Theta` modeling optimized for high performance using `numba`. It also includes a large battery of benchmarking models.

Installation

You can install StatsForecast with:

python pip install statsforecast

or

python conda install -c conda-forge statsforecast

Vist our Installation Guide for further instructions.

Quick Start

Minimal Example

```python from statsforecast import StatsForecast from statsforecast.models import AutoARIMA from statsforecast.utils import AirPassengersDF

df = AirPassengersDF sf = StatsForecast( models=[AutoARIMA(season_length=12)], freq='ME', ) sf.fit(df) sf.predict(h=12, level=[95]) ```

Get Started quick guide

Follow this end-to-end walkthrough for best practices.

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.

Features

• Fastest and most accurate implementations of AutoARIMA, AutoETS, AutoCES, MSTL and Theta in Python.
• Out-of-the-box compatibility with Spark, Dask, and Ray.
• Probabilistic Forecasting and Confidence Intervals.
• Support for exogenous Variables and static covariates.
• Anomaly Detection.
• Familiar sklearn syntax: .fit and .predict.

Highlights

• Inclusion of exogenous variables and prediction intervals for 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.
• 1,000,000 series in 30 min with ray.
• Replace FB-Prophet in two lines of code and gain speed and accuracy. Check the experiments here.
• Fit 10 benchmark models on 1,000,000 series in under 5 min.

Missing something? Please open an issue or write us in

Examples and Guides

📚 End to End Walkthrough: Model training, evaluation and selection for multiple time series

🔎 Anomaly Detection: detect anomalies for time series using in-sample prediction intervals.

👩‍🔬 Cross Validation: robust model’s performance evaluation.

❄️ Multiple Seasonalities: how to forecast data with multiple seasonalities using an MSTL.

🔌 Predict Demand Peaks: electricity load forecasting for detecting daily peaks and reducing electric bills.

📈 Intermittent Demand: forecast series with very few non-zero observations.

🌡️ Exogenous Regressors: like weather or prices

Models

Automatic Forecasting

Automatic forecasting tools search for the best parameters and select the best possible model for a group of time series. These tools are useful for large collections of univariate time series.

|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features| |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:| |AutoARIMA|✅|✅|✅|✅|✅| |AutoETS|✅|✅|✅|✅|| |AutoCES|✅|✅|✅|✅|| |AutoTheta|✅|✅|✅|✅|| |AutoMFLES|✅|✅|✅|✅|✅| |AutoTBATS|✅|✅|✅|✅||

ARIMA Family

These models exploit the existing autocorrelations in the time series.

|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features| |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:| |ARIMA|✅|✅|✅|✅|✅| |AutoRegressive|✅|✅|✅|✅|✅|

Theta Family

Fit two theta lines to a deseasonalized time series, using different techniques to obtain and combine the two theta lines to produce the final forecasts.

|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features| |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:| |Theta|✅|✅|✅|✅|✅| |OptimizedTheta|✅|✅|✅|✅|| |DynamicTheta|✅|✅|✅|✅|| |DynamicOptimizedTheta|✅|✅|✅|✅||

Multiple Seasonalities

Suited for signals with more than one clear seasonality. Useful for low-frequency data like electricity and logs.

|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features| |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:| |MSTL|✅|✅|✅|✅|If trend forecaster supports| |MFLES|✅|✅|✅|✅|✅| |TBATS|✅|✅|✅|✅||

GARCH and ARCH Models

Suited for modeling time series that exhibit non-constant volatility over time. The ARCH model is a particular case of GARCH.

|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features| |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:| |GARCH|✅|✅|✅|✅|| |ARCH|✅|✅|✅|✅||

Baseline Models

Classical models for establishing baseline.

|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features| |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:| |HistoricAverage|✅|✅|✅|✅|| |Naive|✅|✅|✅|✅|| |RandomWalkWithDrift|✅|✅|✅|✅|| |SeasonalNaive|✅|✅|✅|✅|| |WindowAverage|✅||||| |SeasonalWindowAverage|✅|||||

Exponential Smoothing

Uses a weighted average of all past observations where the weights decrease exponentially into the past. Suitable for data with clear trend and/or seasonality. Use the SimpleExponential family for data with no clear trend or seasonality.

|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features| |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:| |SimpleExponentialSmoothing|✅||✅||| |SimpleExponentialSmoothingOptimized|✅||✅||| |SeasonalExponentialSmoothing|✅||✅||| |SeasonalExponentialSmoothingOptimized|✅||✅||| |Holt|✅|✅|✅|✅|| |HoltWinters|✅|✅|✅|✅||

Sparse or Inttermitent

Suited for series with very few non-zero observations

|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |Exogenous features| |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|:----------------:| |ADIDA|✅||✅|✅|| |CrostonClassic|✅||✅|✅|| |CrostonOptimized|✅||✅|✅|| |CrostonSBA|✅||✅|✅|| |IMAPA|✅||✅|✅|| |TSB|✅||✅|✅||

🔨 How to contribute

See CONTRIBUTING.md.

Citing

bibtex @misc{garza2022statsforecast, author={Azul Garza, Max Mergenthaler Canseco, Cristian Challú, Kin G. Olivares}, title = {{StatsForecast}: Lightning fast forecasting with statistical and econometric models}, year={2022}, howpublished={{PyCon} Salt Lake City, Utah, US 2022}, url={https://github.com/Nixtla/statsforecast} }

Contributors ✨

Thanks goes to these wonderful people (emoji key):

azul 💻 🚧	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 💻
Jeroen Van Der Donckt 💻	Roymprog 📖	Nelson Cárdenas Bolaño 📖	Kyle Schmaus 💻	Akmal Soliev 💻	Nick To 💻	Kevin Kho 💻
Yiben Huang 📖	Andrew Gross 📖	taniishkaaa 📖	Manuel Calzolari 💻

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