> ## Documentation Index
> Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt
> Use this file to discover all available pages before exploring further.

# StatsForecast's Models

## 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`](./models.html#autoarima) | ✅ | ✅ | ✅ | ✅ |
| [`AutoETS`](./models.html#autoets) | ✅ | ✅ | ✅ | ✅ |
| [`AutoCES`](./models.html#autoces) | ✅ | ✅ | ✅ | ✅ |
| [`AutoTheta`](./models.html#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`](./models.html#arima) | ✅ | ✅ | ✅ | ✅ |
| [`AutoRegressive`](./models.html#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`](./models.html#theta) | ✅ | ✅ | ✅ | ✅ |
| [`OptimizedTheta`](./models.html#optimizedtheta) | ✅ | ✅ | ✅ | ✅ |
| [`DynamicTheta`](./models.html#dynamictheta) | ✅ | ✅ | ✅ | ✅ |
| [`DynamicOptimizedTheta`](./models.html#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`](./models.html#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`](./models.html#garch) | ✅ | ✅ | ✅ | ✅ |
| [`ARCH`](./models.html#arch) | ✅ | ✅ | ✅ | ✅ |

## Baseline Models

Classical models for establishing baseline.

| Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |
| :- | :-: | :-: | :-: | :-: |
| [`HistoricAverage`](./models.html#historicaverage) | ✅ | ✅ | ✅ | ✅ |
| [`Naive`](./models.html#naive) | ✅ | ✅ | ✅ | ✅ |
| [`RandomWalkWithDrift`](./models.html#randomwalkwithdrift) | ✅ | ✅ | ✅ | ✅ |
| [`SeasonalNaive`](./models.html#seasonalnaive) | ✅ | ✅ | ✅ | ✅ |
| [`WindowAverage`](./models.html#windowaverage) | ✅ | | | |
| [`SeasonalWindowAverage`](./models.html#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`](./models.html#simpleexponentialsmoothing) | ✅ | | | |
| [`SimpleExponentialSmoothingOptimized`](./models.html#simpleexponentialsmoothingoptimized) | ✅ | | | |
| [`Holt`](./models.html#holt) | ✅ | ✅ | ✅ | ✅ |
| [`HoltWinters`](./models.html#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`](./models.html#adida) | ✅ | | | |
| [`CrostonClassic`](./models.html#crostonclassic) | ✅ | | | |
| [`CrostonOptimized`](./models.html#crostonoptimized) | ✅ | | | |
| [`CrostonSBA`](./models.html#crostonsba) | ✅ | | | |
| [`IMAPA`](./models.html#imapa) | ✅ | | | |
| [`TSB`](./models.html#tsb) | ✅ | | | |


## Related topics

- [Cross validation | StatsForecast](/statsforecast/docs/tutorials/crossvalidation.html.md)
- [MLFlow | StatsForecast](/statsforecast/docs/tutorials/mlflow.html.md)
- [Probabilistic Forecasting | StatsForecast](/statsforecast/docs/tutorials/uncertaintyintervals.html.md)
- [Quick Start | StatsForecast](/statsforecast/docs/getting-started/getting_started_short.html.md)
- [Electricity Load Forecast | StatsForecast](/statsforecast/docs/tutorials/electricityloadforecasting.html.md)