Building superposition of deep learning neural networks for solving the problem of time series classification

This paper solves the problem of time series classification using deep learning neural networks. The paper proposes to use a multilevel superposition of models belonging to the following classes of neural networks: two-layer neural networks, Boltzmann machines, and autoencoders. Lower levels of superposition extract informative features from noisy data of high dimensionality, while the upper level of superposition solves the problem of classification based on these extracted features. The proposed model was tested on two samples of physical activity time series. The classification results obtained by the proposed model in the computational experiment were compared with the results which were obtained on the same datasets by foreign authors. The study showed the possibility of using deep learning neural networks for solving problems of physical activity time series classification.