Dynamic management of computer resources is essential for adaptive computing. Adaptive computing systems rely on accurate and robust metric predictors to exploit runtime behavior of programs. In this study, we propose the Unified Prediction Method (UPM) that is system- and metric-independent for predicting computer metrics. Unlike ad-hoc predictors, UPM uses a parametric model and is entirely statistical and data-driven. The parameters of the model are estimated by minimizing an objective function. Choice of the objective function and the model type determines the form of the solution whether it is closed form or numerically determined through optimization. In this study two specific realizations of UPM are presented. The first realization uses mean squared error (MSE) objective function and the second realization uses accumulated squared error (ASE) objective function, in conjunction with autoregressive models. The former objective function leads to Linear Prediction and the latter leads to Predictive Least Square (PLS) prediction. The model parameters for these predictors can be estimated analytically. The prediction is optimal with respect to the chosen objective function. An extensive and rigorous series of prediction experiments for the instruction per cycle and L1 cache miss rate metrics demonstrate superior performance for the proposed predictors over the last value predictor and table based predictor on SPECCPU 2000 benchmarks.