This note uses methods due to Guo, Ljung, and Wang to obtain explicit bounds on the error of the LMS algorithm used in a linear prediction of a signal using previous values of that signal. The signal is assumed to be a mean-zero Gaussian regular stationary random process. The bounds are then used to construct learning curves for the LMS algorithm in situations where the statistics of the process are only partially known.