StatsForecast's Models

Automatic Forecasting
ARIMA Family
Theta Family
Multiple Seasonalities
GARCH and ARCH Models
Baseline Models
Exponential Smoothing
Sparse or Intermittent

Automatic Forecasting

Automatic forecasting tools search for the best parameters and select the best possible model for a series 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
`AutoARIMA`	✅	✅	✅	✅
`AutoETS`	✅	✅	✅	✅
`AutoCES`	✅	✅	✅	✅
`AutoTheta`	✅	✅	✅	✅

ARIMA Family

These models exploit the existing autocorrelations in the time series.

Model	Point Forecast	Probabilistic Forecast	Insample fitted values	Probabilistic fitted values
`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
`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
`MSTL`	✅	✅	✅	✅

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
`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 Intermittent

Suited for series with very few non-zero observations

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

Models Feature engineering

⌘I

Getting Started

Tutorials

How to Guides

Distributed

Experiments

Model References

API Reference

Contributing

StatsForecast's Models

Automatic Forecasting

ARIMA Family

Theta Family

Multiple Seasonalities

GARCH and ARCH Models

Baseline Models

Exponential Smoothing

Sparse or Intermittent

Getting Started

Tutorials

How to Guides

Distributed

Experiments

Model References

API Reference

Contributing

​Automatic Forecasting

​ARIMA Family

​Theta Family

​Multiple Seasonalities

​GARCH and ARCH Models

​Baseline Models

​Exponential Smoothing

​Sparse or Intermittent

Automatic Forecasting

ARIMA Family

Theta Family

Multiple Seasonalities

GARCH and ARCH Models

Baseline Models

Exponential Smoothing

Sparse or Intermittent