# Modeltime Integration

In this tutorial you will learn how to use the **Bayesmodels** package and how to integrate it with the usual **Modeltime** workflow. The main purposes are:

Use an Arima Bayesian model to see how it would apply in the **Bayesmodels** package.

Compare the above model with the classic implementation of the **Modeltime** package through the usual workflow of the escosystem.

`Bayesmodels`

unlocks the following models in one package. Precisely its greatest advantage is to be able to integrate these models with the `Modeltime`

and `Tidymodels`

ecosystems.

**Arima**: `bayesmodels`

connects to the `bayesforecast`

package.

**Garch**: `bayesmodels`

connects to the `bayesforecast`

package.

**Random Walk (Naive)**: `bayesmodels`

connects to the `bayesforecast`

package.

**State Space Model**: `bayesmodels`

connects to the `bayesforecast`

and `bsts`

packages.

**Stochastic Volatility Model**: `bayesmodels`

connects to the `bayesforecast`

package.

**Generalized Additive Models (GAMS)**: `bayesmodels`

connects to the `brms`

package.

**Adaptive Splines Surface**: `bayesmodels`

connects to the `BASS`

package.

**Exponential Smoothing**: `bayesmodels`

connects to the `Rglt`

package.

## The Modeltime Workflow

Hereâ€™s the general process and where the functions fit.