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

机译：从非平稳时间序列数据在线监视和预测复杂的时间序列事件。

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摘要

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 prediction frameworks overcome the limitations of traditional time series analysis techniques, and adapt and innovate data mining concepts to analyzing multivariate time series data. The proposed approaches can be general frameworks to create a set of methods that reveal hidden temporal patterns that are characteristic and predictive of time series events.;In second part of this dissertation provide an overview of the state-of-the-art prediction approaches. In the third part of this dissertation, we perform an extensive data mining study on multivariate EEG data, which indicates that EEG may be predictable for some events. In chapter 4, a reinforcement learning-based online monitoring and prediction framework is introduced and applied to solve the challenging seizure prediction problem from multivariate EEG data. In chapter 5, it first overview of the most popular representation methods for time series data, and then introduce two new robust algorithms for offline and online segmentation of a time series, respectively. Chapter 6 proposes a general online monitoring and prediction framework, which combines temporal feature extraction, feature selection, online pattern identification, and adaptive learning theory to achieve online prediction of complex time series events. Two prediction-rule construction schemes are proposed. In chapter 7, the proposed framework is applied to solve two challenging problems including seizure prediction and 'anxiety' prediction in a simulated driving environment. The significant prediction results demonstrated the superior prediction capability of the proposed framework to predict complex target events from online streams of nonstationary and chaotic time series.
机译：世界上大部分数据供应都是以时间序列的形式出现的。在过去的十年中，对时间序列数据挖掘的兴趣激增。时间序列预测已广泛应用于工程，经济，工业制造，金融，管理等许多领域。已经开发了许多新算法来对时间序列进行分类，聚类，分段，索引，发现规则以及检测异常/新颖性。然而，传统的时间序列分析方法受到时间序列平稳性，残差的正态性和独立性的要求的限制。由于传统时间序列分析方法试图表征和预测所有时间序列观测值，因此无法识别复杂（非周期性，非线性，不规则和混沌）特征。结果，由于高噪声，非平稳性和非线性，对多变量噪声时间序列（例如生理信号）的预测仍然非常具有挑战性。;本研究的目的是开发新的可靠的框架来分析多变量嘈杂的时间序列，并将该框架应用于在线监视嘈杂的时间序列并在线预测关键事件。特别是，这项研究对多元时间序列的一种重要形式进行了广泛的研究：脑电图（EEG）数据，在此基础上，引入并评估了两种新的多元时间序列在线监测和预测框架。新的在线监视和预测框架克服了传统时间序列分析技术的局限性，并适应和创新了数据挖掘概念来分析多元时间序列数据。所提出的方法可以作为通用框架，以创建一组揭示时间序列事件的特征性和预测性的隐藏时间模式的方法。在本论文的第二部分，概述了最新的预测方法。在本文的第三部分，我们对多元脑电数据进行了广泛的数据挖掘研究，这表明脑电图对于某些事件可能是可预测的。在第4章中，介绍了一种基于强化学习的在线监测和预测框架，并将其应用于解决来自多元EEG数据的具有挑战性的癫痫发作预测问题。在第5章中，它首先概述了最流行的时间序列数据表示方法，然后介绍了两种新的健壮算法，分别用于时间序列的离线和在线分割。第6章提出了一种通用的在线监视和预测框架，该框架结合了时间特征提取，特征选择，在线模式识别和自适应学习理论，以实现复杂时间序列事件的在线预测。提出了两种预测规则的构建方案。在第7章中，提出的框架用于解决两个具有挑战性的问题，包括在模拟驾驶环境中的癫痫发作预测和“焦虑”预测。显着的预测结果证明了所提出框架的出色预测能力，可以从非平稳和混沌时间序列的在线流中预测复杂的目标事件。

著录项

  • 作者

    Wang, Shouyi.;

  • 作者单位

    Rutgers The State University of New Jersey - New Brunswick.;

  • 授予单位 Rutgers The State University of New Jersey - New Brunswick.;
  • 学科 Applied Mathematics.;Biology Bioinformatics.;Engineering Industrial.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 229 p.
  • 总页数 229
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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