Online monitoring and prediction of complex time series events from nonstationary time series data

Abstract

OF THE DISSERTATION Online Monitoring and Prediction of Complex Time Series Events From Nonstationary Time Series Data by Shouyi Wang Dissertation Director: Wanpracha Art Chaovalitwongse Much of the world’s supply of data is in the form of time series. In the last decade, there has been an explosion of interest in time series data mining. Time series prediction has been widely used in engineering, economy, industrial manufacturing, finance, management and many other fields. Many new algorithms have been developed to classify, cluster, segment, index, discover rules, and detect anomalies/novelties in time series. However, traditional time series analysis methods are limited by the requirement of stationarity of the time series and normality and independence of the residuals. Because they attempt to characterize and predict all time series observations, traditional time series analysis methods are unable to identify complex (nonperiodic, nonlinear, irregular, and chaotic) characteristics. As a result, the prediction of multivariate noisy time series (such as physiological signals) is still very challenging due to high noise, non-stationarity, and non-linearity. The objective of this research is to develop new reliable frameworks for analyzing multivariate noisy time series, and to apply the framework to online monitor noisy time series and predict critical events online. In particular, this research made an extensive study on one important form of multivariate time series: electroencephalography (EEG) data, based on which two new online monitoring and prediction frameworks for multivariate time series were introduced and evaluated. The new online monitoring and