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用于多变量时间序列检索的深r根秩监督联合二进制嵌入

Deep r‑th Root Rank Supervised Joint Binary Embedding for Multivariate Time Series Retrieval
课程网址: http://videolectures.net/kdd2018_song_time_series_retrieval/  
主讲教师: Dongjin Song
开课单位: NEC美国股份有限公司实验室
开课时间: 2018-11-23
课程语种: 英语
中文简介:
多元时间序列数据在许多现实世界的应用中越来越普遍，例如发电厂监控、医疗保健、可穿戴设备、汽车等。因此，多元时间序列检索，即给定当前的多元时间序列段，如何在历史数据（或数据库）中获得其相关的时间序列段，在许多领域吸引了大量的兴趣。然而，构建这样的系统是具有挑战性的，因为它需要原始时间序列的紧凑表示，该表示可以明确地编码时间动态以及不同时间序列对（传感器）之间的相关性（相互作用）。此外，它要求查询效率，并期望返回的排名列表在顶部具有高精度。尽管已经制定了各种方法，但很少有方法能够共同解决这两个挑战。为了解决这个问题，本文提出了一种秩监督联合二进制嵌入的深r根（Deep-r-RSJBE）来执行多变量时间序列检索。给定一个原始的多变量时间序列片段，我们使用长短期记忆（LSTM）单元对时间动态进行编码，并使用卷积神经网络（CNN）对不同时间序列对（传感器）之间的相关性（相互作用）进行编码。随后，进行联合二进制嵌入，以结合时间动态和相关性。最后，我们开发了一种新的第r根排名损失，以优化Hamming距离排名列表顶部的精度。基于三个公开可用的时间序列数据集的彻底实证研究证明了Deep-r-RSJBE的有效性和效率。
课程简介: Multivariate time series data are becoming increasingly common in numerous real world applications, e.g., power plant monitoring, health care, wearable devices, automobile, etc. As a result, multivariate time series retrieval, i.e., given the current multivariate time series segment, how to obtain its relevant time series segments in the historical data (or in the database), attracts significant amount of interest in many fields. Building such a system, however, is challenging since it requires a compact representation of the raw time series which can explicitly encode the temporal dynamics as well as the correlations (interactions) between different pairs of time series (sensors). Furthermore, it requires query efficiency and expects a returned ranking list with high precision on the top. Despite the fact that various approaches have been developed, few of them can jointly resolve these two challenges. To cope with this issue, in this paper we propose a Deep r-th root of Rank Supervised Joint Binary Embedding (Deep r-RSJBE) to perform multivariate time series retrieval. Given a raw multivariate time series segment, we employ Long Short-Term Memory (LSTM) units to encode the temporal dynamics and utilize Convolutional Neural Networks (CNNs) to encode the correlations (interactions) between different pairs of time series (sensors). Subsequently, a joint binary embedding is pursued to incorporate both the temporal dynamics and the correlations. Finally, we develop a novel r-th root ranking loss to optimize the precision at the top of a Hamming distance ranking list. Thoroughly empirical studies based upon three publicly available time series datasets demonstrate the effectiveness and the efficiency of Deep r-RSJBE.
关 键 词: 多变量时间序列检索; 深r根秩监督; 多元时间序列数据; 卷积神经网络; Deep-r-RSJBE; 多元时间序列检索
课程来源: 视频讲座网
数据采集: 2023-03-27：cyh
最后编审: 2023-03-27：cyh
阅读次数: 31

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