The study of physical activity is important in improving people���s health as it can help people understand the relationship between physical activity and health. Accelerometers, due to its small size, low cost, convenience and its ability to provide objective information about the frequency, intensity, and duration of physical activity, has become the method of choice in measuring physical activity. Machine learning algorithms based on the featurized representation of accelerometer data have become the most widely used approaches in physical activity prediction. To improve the classification accuracy, this thesis first explored the impact of the choice of data (raw vs processed) as well as the choice of features on the performance of various classifiers. The empirical results showed that the machine learning algorithms with strong regularization capabilities always performed better if provided with the most comprehensive feature set extracted from raw accelerometer signal. Based on the hypothesis that for some time series, the most discriminative information could be found at subwindows of various sizes, the Subwindow Ensemble Model (SWEM) was proposed. The SWEM was designed for the accelerometer-based physical activity data, and classified the time series based on the features extracted from subwindows. It was evaluated on six time series datasets. Three of them were accelerometer-based physical activity data, which the SWEM was designed for, and the rest were different types of time series data chosen from other domains. The empirical results indicated a strong advantage of the SWEM over baseline models on the accelerometerbased physical activity data. Further analysis confirmed the hypothesis that the most discriminative features could be extracted from subwindows of different sizes, and they were effectively used by the SWEM. / Graduation date: 2013