> ## 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)                           |        ✅       |                        |                        |                             |