StatsForecast ⚡️

​

Installation

You can install StatsForecast with:

pip install statsforecast

or

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 with this 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.

​

ARIMA Family

These models exploit the existing autocorrelations in the time series.

​

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.

​

Multiple Seasonalities

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

​

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.

​

Baseline Models

Classical models for establishing baseline.

​

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.

​

Sparse or Inttermitent

Suited for series with very few non-zero observations

​

Machine Learning

Leverage exogenous features.

​

How to contribute

See CONTRIBUTING.md.

​

Citing

@misc{garza2022statsforecast,
    author={Federico 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}
}