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Random sampling method for cryptocurrency market time series forecasting
O. E. Sorokoletovaa, T. V. Zakharovaab a 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
b Institute of Informatics Problems, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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.
Keywords:
cryptocurrency, time series, forecasting, classification, metric learning, LSTM, random sampling, neural networks, deep learning.
Received: 08.05.2019
Citation:
O. E. Sorokoletova, T. V. Zakharova, “Random sampling method for cryptocurrency market time series forecasting”, Sistemy i Sredstva Inform., 29:4 (2019), 65–72
Linking options:
https://www.mathnet.ru/eng/ssi672 https://www.mathnet.ru/eng/ssi/v29/i4/p65
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Abstract page: | 188 | Full-text PDF : | 83 | References: | 27 |
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