通过基于递归神经网络的去噪自编码器对相关多元时间序列进行差距估算1

IF 5.8 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Serafín Alonso, Antonio Morán, Daniel Pérez, Miguel A. Prada, Juan J. Fuertes, Manuel Domínguez
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引用次数: 0

摘要

摘要工业领域的技术进步使得安装大量联网传感器成为可能,从而产生了大量高速观测数据。工业 4.0 的到来要求对相关多变量时间序列形式的异构数据具备分析能力。然而,数据缺失会降低处理能力,导致偏差和误解,甚至错误决策。本文提出了一种基于递归神经网络的去噪自动编码器,用于相关多变量时间序列(即表现出时空相关性的序列)中的缺失估算。去噪自编码器(DAE)能够通过学习消除有意添加的间隙来重现输入的缺失数据,而递归神经网络(RNN)则能捕捉时间模式和变量之间的关系。为此,我们在实验中使用三个不同的数据集,对不同的单向(简单 RNN、GRU、LSTM)和双向(BiSRNN、BiGRU、BiLSTM)架构进行了比较,并将其与最先进的方法进行了比较。使用 BiGRU 层的实现方法优于其他方法,它能以较低的重建误差有效填补空白。这种方法适用于多个变量包含长间隙的复杂场景。但是,应避免出现某个变量间隙很短或没有可用数据的极端情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencoder1

Abstract

Technological advances in industry have made it possible to install many connected sensors, generating a great amount of observations at high rate. The advent of Industry 4.0 requires analysis capabilities of heterogeneous data in form of related multivariate time series. However, missing data can degrade processing and lead to bias and misunderstandings or even wrong decision-making. In this paper, a recurrent neural network-based denoising autoencoder is proposed for gap imputation in related multivariate time series, i.e., series that exhibit spatio-temporal correlations. The denoising autoencoder (DAE) is able to reproduce input missing data by learning to remove intentionally added gaps, while the recurrent neural network (RNN) captures temporal patterns and relationships among variables. For that reason, different unidirectional (simple RNN, GRU, LSTM) and bidirectional (BiSRNN, BiGRU, BiLSTM) architectures are compared with each other and to state-of-the-art methods using three different datasets in the experiments. The implementation with BiGRU layers outperforms the others, effectively filling gaps with a low reconstruction error. The use of this approach is appropriate for complex scenarios where several variables contain long gaps. However, extreme scenarios with very short gaps in one variable or no available data should be avoided.

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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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