What and Why are ARCH and GARCH?

When you do time series forecasting you almost always get changes in variance over time.  Sometimes enough to invalidate your decisions and conclusions.  We used these  methods in key ways to produce better results over time.  Somehow I rarely hear these methods mentioned recently.  Here Jason Brownlee provides a good Python based intro.  Fairly non-technical, but coding based.

How to Model Volatility with ARCH and GARCH for Time Series Forecasting in Python by Jason Brownlee   in Time Series

A change in the variance or volatility over time can cause problems when modeling time series with classical methods like ARIMA.

The ARCH or Autoregressive Conditional Heteroskedasticity method provides a way to model a change in variance in a time series that is time dependent, such as increasing or decreasing volatility. An extension of this approach named GARCH or Generalized Autoregressive Conditional Heteroskedasticity allows the method to support changes in the time dependent volatility, such as increasing and decreasing volatility in the same series.

In this tutorial, you will discover the ARCH and GARCH models for predicting the variance of a time series.

After completing this tutorial, you will know:

The problem with variance in a time series and the need for ARCH and GARCH models.
How to configure ARCH and GARCH models.
How to implement ARCH and GARCH models in Python.
Let’s get started.   .... "