时间序列中使用自编码器的模式发现与非学习方法的比较

IF 5.8 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
F. Noering, Yannik Schröder, K. Jonas, F. Klawonn
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引用次数: 6

摘要

在技术系统中,对类似情况的分析是一种很有前途的技术,可以获得有关系统状态、健康或磨损的信息。通常情况下,无法定义情况,但需要在所考虑的系统的时间序列数据中发现反复出现的模式。本文讨论了发现时间序列中频繁变长模式的不同方法的评估。由于人工神经网络在各个研究领域的成功,这项工作的一个特殊问题是神经网络在时间序列中模式发现问题的适用性。因此,我们应用并调整了卷积自编码器,并将其与基于动态时间翘曲、基于时间序列离散化以及基于矩阵轮廓的经典非学习方法进行了比较。这些非学习方法也经过了调整,以满足我们的需求,比如从噪声时间序列中发现潜在的时间尺度模式。我们在使用合成数据集的广泛测试中展示了这些方法的性能(质量、计算时间、参数化的努力)。此外,通过使用实际车辆数据测试了对其他数据集的可移植性。我们展示了卷积自编码器以无监督的方式发现模式的能力。此外,测试表明,自编码器能够发现与经典非学习方法相似的质量模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pattern discovery in time series using autoencoder in comparison to nonlearning approaches
In technical systems the analysis of similar situations is a promising technique to gain information about the system’s state, its health or wearing. Very often, situations cannot be defined but need to be discovered as recurrent patterns within time series data of the system under consideration. This paper addresses the assessment of different approaches to discover frequent variable-length patterns in time series. Because of the success of artificial neural networks (NN) in various research fields, a special issue of this work is the applicability of NNs to the problem of pattern discovery in time series. Therefore we applied and adapted a Convolutional Autoencoder and compared it to classical nonlearning approaches based on Dynamic Time Warping, based on time series discretization as well as based on the Matrix Profile. These nonlearning approaches have also been adapted, to fulfill our requirements like the discovery of potentially time scaled patterns from noisy time series. We showed the performance (quality, computing time, effort of parametrization) of those approaches in an extensive test with synthetic data sets. Additionally the transferability to other data sets is tested by using real life vehicle data. We demonstrated the ability of Convolutional Autoencoders to discover patterns in an unsupervised way. Furthermore the tests showed, that the Autoencoder is able to discover patterns with a similar quality like classical nonlearning approaches.
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来源期刊
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering 工程技术-工程:综合
CiteScore
9.90
自引率
21.50%
发文量
21
审稿时长
>12 weeks
期刊介绍: Integrated Computer-Aided Engineering (ICAE) was founded in 1993. "Based on the premise that interdisciplinary thinking and synergistic collaboration of disciplines can solve complex problems, open new frontiers, and lead to true innovations and breakthroughs, the cornerstone of industrial competitiveness and advancement of the society" as noted in the inaugural issue of the journal. The focus of ICAE is the integration of leading edge and emerging computer and information technologies for innovative solution of engineering problems. The journal fosters interdisciplinary research and presents a unique forum for innovative computer-aided engineering. It also publishes novel industrial applications of CAE, thus helping to bring new computational paradigms from research labs and classrooms to reality. Areas covered by the journal include (but are not limited to) artificial intelligence, advanced signal processing, biologically inspired computing, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, intelligent and adaptive systems, internet-based technologies, knowledge discovery and engineering, machine learning, mechatronics, mobile computing, multimedia technologies, networking, neural network computing, object-oriented systems, optimization and search, parallel processing, robotics virtual reality, and visualization techniques.
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