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Installation

You can install StatsForecast with: 
pip install statsforecast
or 
conda install -c conda-forge statsforecast
Vist our Installation Guide for further instructions.

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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.

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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.

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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.

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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 Slack

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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

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Models

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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.
ModelPoint ForecastProbabilistic ForecastInsample fitted valuesProbabilistic fitted valuesExogenous features
AutoARIMA✅✅✅✅✅
AutoETS✅✅✅✅
AutoCES✅✅✅✅
AutoTheta✅✅✅✅
AutoMFLES✅✅✅✅✅
AutoTBATS✅✅✅✅

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ARIMA Family

These models exploit the existing autocorrelations in the time series.
ModelPoint ForecastProbabilistic ForecastInsample fitted valuesProbabilistic fitted valuesExogenous features
ARIMA✅✅✅✅✅
AutoRegressive✅✅✅✅✅

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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.
ModelPoint ForecastProbabilistic ForecastInsample fitted valuesProbabilistic fitted valuesExogenous features
Theta✅✅✅✅
OptimizedTheta✅✅✅✅
DynamicTheta✅✅✅✅
DynamicOptimizedTheta✅✅✅✅

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Multiple Seasonalities

Suited for signals with more than one clear seasonality. Useful for low-frequency data like electricity and logs.
ModelPoint ForecastProbabilistic ForecastInsample fitted valuesProbabilistic fitted valuesExogenous features
MSTL✅✅✅✅If trend forecaster supports
MFLES✅✅✅✅✅
TBATS✅✅✅✅

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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.
ModelPoint ForecastProbabilistic ForecastInsample fitted valuesProbabilistic fitted valuesExogenous features
GARCH✅✅✅✅
ARCH✅✅✅✅

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Baseline Models

Classical models for establishing baseline.
ModelPoint ForecastProbabilistic ForecastInsample fitted valuesProbabilistic fitted valuesExogenous features
HistoricAverage✅✅✅✅
Naive✅✅✅✅
RandomWalkWithDrift✅✅✅✅
SeasonalNaive✅✅✅✅
WindowAverage✅
SeasonalWindowAverage✅

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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.
ModelPoint ForecastProbabilistic ForecastInsample fitted valuesProbabilistic fitted valuesExogenous features
SimpleExponentialSmoothing✅
SimpleExponentialSmoothingOptimized✅
SeasonalExponentialSmoothing✅
SeasonalExponentialSmoothingOptimized✅
Holt✅✅✅✅
HoltWinters✅✅✅✅

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Sparse or Inttermitent

Suited for series with very few non-zero observations
ModelPoint ForecastProbabilistic ForecastInsample fitted valuesProbabilistic fitted valuesExogenous features
ADIDA✅✅✅
CrostonClassic✅✅✅
CrostonOptimized✅✅✅
CrostonSBA✅✅✅
IMAPA✅✅✅
TSB✅✅✅

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Machine Learning

Leverage exogenous features.
ModelPoint ForecastProbabilistic ForecastInsample fitted valuesProbabilistic fitted valuesExogenous features
SklearnModel✅✅✅✅

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How to contribute

See CONTRIBUTING.md.

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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}
}