Temporal data analysis has been widely applied in various areas, such as bio-informatics, outlier detection, and trajectory mining. These applications require a variety of machine learning methods, either supervised or unsupervised. In this thesis, we study several supervised and unsupervised learning methods suitable for our target applications. The first application is to monitor the battery level in wireless communication devices, where we develop a time series classification method to identify the working status of the devices. The classifier achieves a high accuracy, but incurs heavy annotation cost in preparing training data. To solve this problem, we propose an efficient and effective active learning method. Specifically, we adapt the idea of shapelet discovery and select...[ Read more ]

Temporal data analysis has been widely applied in various areas, such as bio-informatics, outlier detection, and trajectory mining. These applications require a variety of machine learning methods, either supervised or unsupervised. In this thesis, we study several supervised and unsupervised learning methods suitable for our target applications. The first application is to monitor the battery level in wireless communication devices, where we develop a time series classification method to identify the working status of the devices. The classifier achieves a high accuracy, but incurs heavy annotation cost in preparing training data. To solve this problem, we propose an efficient and effective active learning method. Specifically, we adapt the idea of shapelet discovery and select the training data based on both the uncertainty and the utility of each data instance. This method outperforms the state-of-the-art active learning methods on time series data. The second application is to study the patterns in movement trajectories. In particular, we propose an unsupervised method to identify trajectory patterns that match well-known team strategies in professional basketball games. Our experimental results demonstrate the effectiveness of our proposed method in comparison with traditional methods.