Covariance functions are important in predicting and filtering time series data. This study shows how different types of covariance functions, like exponential and oscillating functions, can be linked to autoregressive moving average (ARMA) processes. By doing this, the researchers found ways to make these functions more flexible and versatile for modeling complex correlations. They also identified conditions that need to be met for these functions to work well in predicting spatial data.