Covariances are important in time series analysis for various tasks like prediction and hypothesis testing. This chapter reviews how to estimate covariance matrices in both low and high dimensions. The researchers looked at sample covariances and how well they estimate the true covariance matrix. They found that high-dimensional covariance matrix estimates in time series have specific properties, and the estimates are consistent for estimated parameters.