> ## Documentation Index
> Fetch the complete documentation index at: https://nixtla-feat-posthog-analytics.mintlify.site/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)                           |        ✅       |                        |                        |                             |