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Adaptive computing systems rely on accurate predictions of workload behavior to understand and respond to the dynamically-varying application characteristics. In this study, we propose a Statistical Metric Model (SMM) that is system-and metric-independent for predicting workload behavior. SMM is a probability distribution over workload patterns and it attempts to model how frequently a specific behavior occurs. Maximum Likelihood Estimation (MLE) criterion is used to estimate the parameters of the SMM. The model parameters are further refined with a smoothing method to improve prediction robustness. The SMM learns the application patterns during runtime as applications run, and at the same time predicts the upcoming program phases based on what it has learned so far. An extensive and rigorous series of prediction experiments demonstrates the superior performance of the SMM predictor over existing predictors on a wide range of benchmarks. For some of the benchmarks, SMM improves prediction accuracy by 10X and 3X, compared to the existing last-value and table-based prediction approaches respectively. SMM's improved prediction accuracy results in superior power-performance trade-offs when it is applied to dynamic power management.