  This article presents a method for recovering missing values in
multidimensional time series. The method combines neural network technologies
and an algorithm for searching snippets (behavioral patterns of a time series).
It includes the stages of data preprocessing, recognition and reconstruction,
using convolutional and recurrent neural networks. Experiments have shown high
accuracy of recovery and the advantage of the method over SOTA methods.
