This document presents a general framework for enhancing time series prediction performance. It discusses using multiple predictions from a base method like neural networks, ARIMA or Holt-Winters to improve accuracy. Short-term enhancement uses support vector regression on statistic and reliability features of the multiple predictions to enhance 1-step ahead predictions. Long-term enhancement trains additional models on the short-term predictions to enhance longer-horizon predictions. The framework is evaluated on traffic flow data with prediction horizons of 1 week and 13 weeks.