GARCH models are better for analyzing financial time series with volatility clustering, while ARIMA is more suited for forecasting data with clear trends and patterns—choosing depends on the data’s characteristics and whether volatility modeling is essential.

The YouTube referred to above as part of the question answers well ; ARIMA is bases on mean or average values of the time.series : while GARCH is based on variance of the present and past values .GARCH has a long past memory and variance clustering which is often a characteristic of financial markets where variance is treated as a proxy for risk .ARIMA can refer to the return of financial market and GARCH for the risk of return .But then financial markets may not give a stable return as will be predicted by ARIMA models.

A GARCH model is an ARMA model applied to modelling variance or volatility. If you understand this, I find it difficult to comprehend your question.

The implementation of ARIMA and GARCH models can vary across different software packages. If this is the cause of your problem, you should ask a more specific question.

It is possible to model a time series using ARIMA for the level and GARCH for the variance.

ARIMA models forecast mean trends, while GARCH models forecast changing variance (volatility). Use ARIMA for predictable, stable trends and GARCH when modeling financial data with volatility clustering.