Finding semantics in time series

In order to understand a complex system, we analyze its output or its log data. For example, we track a system's resource consumption (CPU, memory, message queues of different types, etc) to help avert system failures; we examine economic indicators to assess the severity of a recession; we monitor a patient's heart rate or EEG for disease diagnosis. Time series data is involved in many such applications. Much work has been devoted to pattern discovery from time series data, but not much has attempted to use the time series data to unveil a system's internal dynamics. In this paper, we go beyond learning patterns from time series data. We focus on obtaining a better understanding of its data generating mechanism, and we regard patterns and their temporal relations as organic components of the hidden mechanism. Specifically, we propose to model time series data using a novel pattern-based hidden Markov model (pHMM), which aims at revealing a global picture of the system that generates the time series data. We propose an iterative approach to refine pHMMs learned from the data. In each iteration, we use the current pHMM to guide time series segmentation and clustering, which enables us to learn a more accurate pHMM. Furthermore, we propose three pruning strategies to speed up the refinement process. Empirical results on real datasets demonstrate the feasibility and effectiveness of the proposed approach.