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COMPACT REPRESENTATION AND TIME SERIES SEGMENT RETRIEVAL THROUGH DEEP LEARNING

机译:通过深度学习,紧凑的表示和时间序列段检索

摘要

Systems and methods for retrieving similar multivariate time series segments are provided. The systems and methods include extracting a long feature vector and a short feature vector from a time series segment, converting the long feature vector into a long binary code, and converting the short feature vector into a short binary code. The systems and methods further include obtaining a subset of long binary codes from a binary dictionary storing dictionary long codes based on the short binary codes, and calculating similarity measure for each pair of the long feature vector with each dictionary long code. The systems and methods further include identifying a predetermined number of dictionary long codes having the similarity measures indicting a closest relationship between the long binary codes and dictionary long codes, and retrieving a predetermined number of time series segments associated with the predetermined number of dictionary long codes.
机译:提供了用于检索类似的多变量时间序列段的系统和方法。 系统和方法包括从时间序列段中提取长特征向量和短特征向量,将长特征向量转换为长二进制代码,并将短特征向量转换为短二进制代码。 系统和方法还包括基于短二进制代码从二进制字典存储来自二进制字典的长二进制代码的子集,以及用每个字典长码计算每对长特征向量的每对长特征向量的相似度度量。 系统和方法还包括识别具有相似性测量的预定数量的字典长代码,其指定长二进制代码和字典长代码之间最接近的关系,以及检索与预定数量的字典长代码相关联的预定时间序列段 。

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