USING LSTM NETWORK FOR SOLVING THE MULTIDIMENTIONAL TIME SERIES FORECASTING PROBLEM

Authors

  • M. Obrubov "Vladimir State University named after Alexander Grigorievich and Nikolai Grigorievich Stoletovs" (VlSU)
  • S. Kirillova "Vladimir State University named after Alexander Grigorievich and Nikolai Grigorievich Stoletovs" (VlSU)

DOI:

https://doi.org/10.31618/nas.2413-5291.2021.2.68.450

Keywords:

neural network; prediction; multidimensional time series.

Abstract

The article discusses using of the recurrent neural networks technology to the multidimensional time series prediction problem. There is an experimental determination of the neural network architecture and its main hyperparameters carried out to achieve the minimum error. The revealed network structure going to be used further to detect anomalies in multidimensional time series.

Author Biographies

M. Obrubov , "Vladimir State University named after Alexander Grigorievich and Nikolai Grigorievich Stoletovs" (VlSU)

2nd year master's student of the Department of Information Systems and Software Engineering

S. Kirillova , "Vladimir State University named after Alexander Grigorievich and Nikolai Grigorievich Stoletovs" (VlSU)

candidate of technical sciences, professor

References

Dozat, T. Incorporating Nesterov Momentum into Adam / T. Dozat // ICLR Workshop. — 2016.

Dupond, S. A thorough review on the current advance of neural network structures / S. Dupond // Annual Reviews in Control. — 2019. — Vol. 14. — P. 200-230.

Kingma, D. Adam: A Method for Stochastic Optimization / D. Kingma, J. Ba // ICLR. — 2014.

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Published

2021-07-01

Issue

Vol. 2 No. 68 (2021)

Section

Статьи

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