Solved: Why use autocorrelation instead of autocovariance when examining stationary time series?

In a stationary time series data are challenging and are likely to change considering the assumptions of the liear programming. In such a situation the residual errors can be corrrelated inorder to eliminate the inconsistancy. When the autocovariance is utilized its variables can change over time which can lead to an invalid results. In stationary time series the statistical properties of the data do not change considerably over time. Hence auto correlation will be much applicable when compared to the acut covariace.