Research and Application of Deep Neural Network Architectures for Classification on Multidimensional Time Series

被引：0

作者：

Esenkov, A. S. ^{[1
]}

Zakharova, E. M. ^{[2
]}

Kovaleva, M. D. ^{[2
]}

Konstantinov, D. E. ^{[2
]}

Makarov, I. S. ^{[1
]}

Pankovets, E. A. ^{[2
]}

机构：

[1] Russian Acad Sci, Fed Res Ctr Informat & Control, Moscow 119333, Russia

[2] Natl Res Univ, Moscow Inst Phys & Technol, Dolgoprudnyi 141701, Moscow Oblast, Russia

来源：

JOURNAL OF COMPUTER AND SYSTEMS SCIENCES INTERNATIONAL | 2022年 / 61卷 / 04期

关键词：

D O I：

10.1134/S1064230722040074

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The results of studying the architectures of deep neural networks designed to solve classification problems are presented. As a result, attributes are formed for effective decision-making automation. Multidimensional time series of financial markets are used as data. The problems of binary and multiple classification are considered. Fully connected, recurrent (long short-term memory (LSTM)) and hybrid combined architectures of neural networks are analyzed. The studied multivariate time series is obtained by combining a one-dimensional time series of asset value, trading volume, technical indicators, and other parameters.

引用

页码：616 / 625

页数：10

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