The design of GRU-based cell structure robust to missing value and noise of time-series data in recurrent neural network

摘要

To provide a recursive artificial neural network model capable of simultaneously imputing missing values of time series data and mitigating noise according to the problem to be predicted, (a) mitigating noise by a weighted average method using a noise mitigation filter that can be learned from time series data , (b) replacing missing values, and (c) storing information that needs to be memorized at the current point in time through GRU operation in a latent state vector are all included in a single cell structure. In addition, in the present invention, in constructing a recursive neural network model, in the step (a), in the process of learning the recursive neural network model so that the weight parameter for noise mitigation included in the cell structure is suitable for the prediction task, It is characterized in that it is learned to be optimized for the task. By the above method, a recursive artificial neural network model that simultaneously performs replacement of missing values and noise mitigation of time series data without separate preprocessing can be utilized for various machine learning tasks.