GRU-BASED CELL STRUCTURE DESIGN ROBUST TO MISSING DATA AND NOISE IN TIME SERIES DATA IN RECURRENT NEURAL NETWORK

摘要

Provided is a recurrent artificial neural network model capable of imputing a missing value and reducing noise, simultaneously, in time series data in accordance to a problem being predicted, the recurrent artificial neural network model comprising, in a single cell structure, all of the steps of: (a) reducing noise in time series data by means of a weighted average method using a learnable noise reduction filter; (b) imputing a missing value; and (c) storing, in a hidden state vector, information which must be memorized at the present time through GRU computation. In addition, in configuring the recurrent artificial neural network model, the present invention is characterized in that, in step (a), a weighted parameter for reducing noise included in a cell structure is learned so as to be optimized for a task in a process of training the recurrent artificial neural network model to be adequate for a prediction task. By means of such method, the recurrent artificial neural network model performing missing value imputation and noise reduction, simultaneously, for time series data without separate preprocessing may be used for various machine learning tasks.