Statistical ⚡️ Forecast

Installation

You can install StatsForecast with:

pip install statsforecast

conda install -c conda-forge statsforecast

Vist our Installation Guide for further instructions.

Quick Start

Minimal Example

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.
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
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
SimpleExponentialSmoothing	✅		✅
SimpleExponentialSmoothingOptimized	✅		✅
SeasonalExponentialSmoothing	✅		✅
SeasonalExponentialSmoothingOptimized	✅		✅
Holt	✅	✅	✅	✅
HoltWinters	✅	✅	✅	✅

Sparse or Inttermitent

Suited for series with very few non-zero observations

Model	Point Forecast	Insample fitted values	Probabilistic fitted values
ADIDA	✅	✅	✅
CrostonClassic	✅	✅	✅
CrostonOptimized	✅	✅	✅
CrostonSBA	✅	✅	✅
IMAPA	✅	✅	✅
TSB	✅	✅	✅

🔨 How to contribute

See CONTRIBUTING.md.

Citing

@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} 💻