Systems and Means of Informatics
2019, Volume 29, Issue 4, pp 65-72
RANDOM SAMPLING METHOD FOR CRYPTOCURRENCY MARKET TIME SERIES FORECASTING
- O. E. Sorokoletova
- T. V. Zakharova
Abstract
This paper applies Random Sampling Method (RSM) to classification task for cryptocurrencies time series, which are not-stationary Long Short Term Memory (LSTM) networks have been demonstrated to be particularly useful for learning sequences containing longer term patterns of unknown length, such as at this task. But RSM represents another deep learning algorithm with more
flexible architecture, built on the basis of LSTM cells and thus having all the advantages of the traditional algorithm, but more resistant to the class imbalance problem. The main distinguishing feature of RSM is the use of metric learning and special sampling scheme.
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[+] About this article
Title
RANDOM SAMPLING METHOD FOR CRYPTOCURRENCY MARKET TIME SERIES FORECASTING
Journal
Systems and Means of Informatics
Volume 29, Issue 4, pp 65-72
Cover Date
2019-11-30
DOI
10.14357/08696527190406
Print ISSN
0869-6527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
cryptocurrency; time series; forecasting; classification; metric learning; LSTM; random sampling; neural networks; deep learning
Authors
O. E. Sorokoletova  and T. V. Zakharova , 
Author Affiliations
Department of Mathematical Statistics, Faculty of Computational Mathematics and Cybernetics, M.V. Lomonosov Moscow State University, 1-52 Leninskiye Gory, GSP-1, Moscow 119991, Russian Federation
Institute of Informatics Problems, Federal Research Center "Computer Science
and Control", Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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