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Time-series data feature extraction device, time-series data feature extraction method, and time-series data feature extraction program

机译:时间序列数据特征提取装置,时间序列数据特征提取方法以及时间序列数据特征提取程序

摘要

A time-series-data feature extraction device includes: a data processing unit that processes a received unevenly spaced time-series-data group into an evenly spaced time-series-data group including omissions and an omission information group indicating presence or absence of omissions, based on a received input time-series data length and a received minimum observation interval; a model learning unit that learns a weight vector of each layer of a model with a difference between an element not missing in a matrix of the evenly spaced time-series-data group including omissions and an element of an output result of an output layer of the model being taken as an error, and stores the weight vector as a model parameter in a storage unit, the difference being; and a feature extraction unit that receives time-series data of a feature extraction target, calculates a value of the intermediate layer of the model with use of the model parameter stored in the storage unit by inputting the received time-series data of the feature extraction target into the model, and outputs the calculated value of the intermediate layer as a feature that represents temporal changes in data.
机译:时序数据特征提取装置包括:数据处理单元,其将接收到的不均匀间隔的时序数据组处理为包括遗漏的均匀间隔的时序数据组和指示存在或不存在遗漏的遗漏信息组。 ,基于接收到的输入时间序列数据长度和接收到的最小观察间隔;模型学习单元,其学习模型的每一层的权重向量,其中包括遗漏的均匀间隔时间序列数据组的矩阵中不缺少的元素与该元素的输出层的输出结果的元素之间的差将该模型作为误差,并将权重向量作为模型参数存储在存储单元中,其差为;接收特征提取目标的时间序列数据的特征提取单元,通过输入接收到的特征提取的时间序列数据,使用存储在存储单元中的模型参数来计算模型的中间层的值将其作为模型的目标,并输出中间层的计算值作为代表数据随时间变化的特征。

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