Accurate estimation of energy expenditure (EE) in children and adolescents is required to better understand physiological, behavioral and environmental factors affecting energy balance. Cross-sectional time series (CSTS) models which account for correlation structure of repeated observations on the same individual may be advantageous for prediction of EE. Methods: CSTS models for prediction of minute-by-minute EE and hence TEE from heart rate (HR), physical activity (PA) measured by accelerometry and observable subject variables were developed in 109 children and adolescents using Actiheart and 24-h room respiration calorimetry. Results: CSTS models based on HR, PA, time invariant covariates and interactions were developed. These dynamic models involve lagged and lead values of HR, and lagged values of PA to better describe the series of minute-by-minute EE. CSTS models with random intercepts and random slopes were investigated. For comparison, likelihood ratio tests were used. Log likelihood increased substantially when random slopes for HR and PA were added. The population-specific model uses HR and 1-minute and 2-minute lagged and lead values of HR, HR², PA, and 1-minute and 2-minute lagged values of PA, PA², age, age², gender, weight, height, minimum HR, sitting HR, HRxheight, HRxweight, HRxage, PAxweight and PAxgender interactions (P-values<0.001). Mean±SD percent prediction error for TEE was 0.9±10.3%. Errors were not correlated with age, weight, height or body mass index. Conclusion: CSTS modeling provides a useful predictive model for EE and hence TEE in children and adolescents based on HR and PA and other observable explanatory subject characteristics of age, gender, weight and height.