The linear prediction coefficients are a set of coefficients that are used in linear prediction analysis to estimate future values of a time series based on past observations. $ \hat{s}(n) = \sum_{k=1}^{P} a_k s(n-k) $ The error between the predicted value and the actual value is called the prediction error or [[residue]]: $ e(n) = s(n) - \hat{s}(n) $ The linear prediction coefficients $a_{k}$ can be computed by [[minimizing the energy of the residue]].