On One Method of Imputation Missing Values of a Streaming Time Series in Real Time

Abstract

The problem of the imputation of missing values in a streaming time series arises in a wide range of Industry 4.0 and Internet of Things applications. In the article, we propose a novel imputation method based on time series mining techniques and artificial neural networks. The method involves three steps of imputation: data preprocessing, recognition, and reconstruction. Preprocessing is a one-time preparation of training data samples. Recognition and reconstruction are implemented through two neural networks trained on the samples above. Preprocessing supposes the discovery of a set of typical subsequences (snippets) in a pre-stored fragment of the streaming time series without misses. Recognition is implemented through a Convolutional Neural Network, and its input is a vector of the elements preceding the current (missing) value. The Recognizer outputs the snippet that the input subsequence is most similar to. Reconstruction is implemented through a Recurrent Neural Network, and its input is a concatenation of the Recognizer's output and the vector of the elements preceding the missing value. The Reconstructor outputs the value to be imputed. The experimental results show high accuracy and the advantage of the proposed method over analogs.