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J. Risk Financial Manag. 2019, 12(1), 17; https://doi.org/10.3390/jrfm12010017

Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning

Graduate School of Arts and Sciences, International Christian University, Osawa 3-10-2, Mitaka, Tokyo 181-8585, Japan
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Received: 25 December 2018 / Revised: 15 January 2019 / Accepted: 17 January 2019 / Published: 21 January 2019
(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)
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Abstract

We provide a trend prediction classification framework named the random sampling method (RSM) for cryptocurrency time series that are non-stationary. This framework is based on deep learning (DL). We compare the performance of our approach to two classical baseline methods in the case of the prediction of unstable Bitcoin prices in the OkCoin market and show that the baseline approaches are easily biased by class imbalance, whereas our model mitigates this problem. We also show that the classification performance of our method expressed as the F-measure substantially exceeds the odds of a uniform random process with three outcomes, proving that extraction of deterministic patterns for trend classification, and hence market prediction, is possible to some degree. The profit rates based on RSM outperformed those based on LSTM, although they did not exceed those of the buy-and-hold strategy within the testing data period, and thus do not provide a basis for algorithmic trading. View Full-Text
Keywords: cryptocurrency; metric learning; classification framework; time series; trend prediction cryptocurrency; metric learning; classification framework; time series; trend prediction
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Shintate, T.; Pichl, L. Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning. J. Risk Financial Manag. 2019, 12, 17.

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