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DYNAMIC DISCOVERY OF DEPENDENCIES AMONG TIME SERIES DATA USING NEURAL NETWORKS

机译:基于神经网络的时间序列数据的动态发现

摘要

Techniques for determining temporal dependencies and inter-time series dependencies in multi-variate time series data are provided. For example, embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor that can execute the computer executable components stored in the memory. The computer executable components can include: a computing component that encodes recurrent neural networks (RNNs) with time series data and determines decoded RNNs based on temporal context vectors, to determine temporal dependencies in time series data; a combining component that combines the decoded RNNs and determines an inter-time series dependence context vector and an RNN dependence decoder; and an analysis component that determines inter-time series dependencies in the time series data and forecast values for the time series data based on the inter-time series dependence context vector and the RNN dependence decoder.
机译:提供了用于确定多元时间序列数据中的时间依赖性和时间序列间依赖性的技术。例如,本文描述的实施例可以包括系统,该系统可以包括可以存储计算机可执行组件的存储器。该系统还可以包括处理器,该处理器可以执行存储在存储器中的计算机可执行组件。所述计算机可执行组件可以包括:计算组件,其利用时间序列数据对循环神经网络(RNN)进行编码,并且基于时间上下文向量来确定解码的RNN,以确定时间序列数据中的时间依赖性。合并组件,其合并解码后的RNN并确定时间间序列依赖性上下文向量和RNN依赖性解码器;分析组件,其基于时间序列间相关性上下文矢量和RNN相关性解码器确定时间序列数据中的时间序列间相关性和时间序列数据的预测值。

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