形状变换在地震、风浪数据时间序列分类中的应用（CS LG）---刘持诚

由于对大量工程结构（包括建筑物、桥梁、塔楼和海上平台等）的长期健康监测，使用时间序列分类法从大型数据库中自主检测所需事件，在土木工程中越来越重要。在这种情况下，本文提出了一种相对较新的时间序列表示方法，即 “Shapelet变换”，它是基于时间序列子序列的局部相似性来进行的。考虑到地震、风能和海洋工程中的时间序列信号的独特属性，该变换的应用产生了一种新的基于形状的特征表示。将这种基于形状的特征表示与标准的机器学习算法结合起来，提出了一个真正的“白箱”机器学习模型，具有可理解的特征和透明的算法。该模型可以自动检测事件，无需领域从业人员的干预，从而产生了一个实用的事件检测程序。通过实例证明了这种基于形状变换的自主检测程序的有效性，从连续记录的地动测量数据中识别已知和未知的地震事件；从地动的速度时间历史中检测出脉冲，以区分近场和远场地动；从连续的风速测量中识别雷暴；从桥梁监测数据中检测大振幅的风诱导振动；以及识别对海上结构有重大影响的断裂波。

原文题目：Applications of shapelet transform to time series classification of earthquake, wind and wave data

原文：Autonomous detection of desired events from large databases using time series classification is becoming increasingly important in civil engineering as a result of continued long-term health monitoring of a large number of engineering structures encompassing buildings, bridges, towers, and offshore platforms. In this context, this paper proposes the application of a relatively new time series representation named "Shapelet transform", which is based on local similarity in the shape of the time series subsequences. In consideration of the individual attributes distinctive to time series signals in earthquake, wind and ocean engineering, the application of this transform yields a new shape-based feature representation. Combining this shape-based representation with a standard machine learning algorithm, a truly "white-box" machine learning model is proposed with understandable features and a transparent algorithm. This model automates event detection without the intervention of domain practitioners, yielding a practical event detection procedure. The efficacy of this proposed shapelet transform-based autonomous detection procedure is demonstrated by examples, to identify known and unknown earthquake events from continuously recorded ground-motion measurements, to detect pulses in the velocity time history of ground motions to distinguish between near-field and far-field ground motions, to identify thunderstorms from continuous wind speed measurements, to detect large-amplitude wind-induced vibrations from the bridge monitoring data, and to identify plunging breaking waves that have a significant impact on offshore structures.

原文作者：Monica Arul, Ahsan Kareem

原文地址：https://arxiv.org/abs/2004.11243