Time series data mining of structure changes using dynamic systems
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This research emphasizes discovery of important changes in structure of time series data. These structural changes imply the loss of customary patterns and the appearance of a novel pattern that has not been experienced previously. Most previous work in this area has been concentrated on identifying previously known or predefined patterns. The major distinction of my research is that the framework offers the ability to discover internal structural changes of time series online dynamically without statistical assumptions about data. The internal structure within time series can be used to improve solutions and provide important insights into the problem domains. Analysis of structured time series data is widely used for many applications, such as economic forecasting, stock market analysis, and networks, etc. This dissertation introduces our research on high-precision modeling, prediction, similarity matching of time series data with consideration of internal structures of data. Our objectives include: (i) formulating a framework for online dynamic gray modeling of time series data streams, (ii) analyzing and characterize the structure of time series stream data using reference and test models, (iii) developing real-time prediction methods based on the online modeling results of corresponding internal structure, and (iv) developing a real-time online similarity matching method that considers the identified internal structures of the time series. We have developed an integrated online structural changes mining (SCM) framework to achieve these objectives. The framework is composed of (a) a dynamic gray model (DGM) that captures the internal structure of time series data online, (b) an algorithm that whitens the incoming data into structures based on a reference model and a test model according to the underlying DGM, (c) a set of analytical methods for data analysis, including online subsequence matching, which generates dynamic query subsequences, defines new subsequence similarity measures, and performs similarity matching with consideration of the internal structures of time series data. This framework is very useful for real-time systems where response time is critical. We have applied the framework to multiple problem domains, such as financial data analysis and network traffic analysis.