Hi @itslarajean , stationarity refers to the series moving around a fixed mean, this is important in forecasting as you're trying to predict future values based on the self-relationship of the series. Typically this is done by modelling the autoregression (relationship between x_t, x_t-1...) and the moving average (which models the impact of exogenous shocks to the series).
Where data is already stationary, one might use an ARMA model. If the data needs differencing to make it stationary that's when the more generalised ARIMA is used. With regards to heteroskedasticity this is where there is some stochastic volatility in the series (such as financial time series) you may use a model like ARCH which models the variance of the error term. It would be worth going through some online resources to familiarise yourself with some of the different modelling techniques and their intuition. The Wiki page for Time Series is a good starting point https://en.wikipedia.org/wiki/Time_series
