Time Series Classification of Cryptocurrency Price Trend Based on a Recurrent LSTM Neural Network

Do-Hyung Kwon, Ju-Bong Kim, Ju-Sung Heo, Chan-Myung Kim and Youn-Hee Han
Volume: 15, No: 3, Page: 694 ~ 706, Year: 2019
10.3745/JIPS.03.0120
Keywords: Classification, Gradient Boosting, Long Short-Term Memory, Time Series Analysis
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Abstract
In this study, we applied the long short-term memory (LSTM) model to classify the cryptocurrency price time series. We collected historic cryptocurrency price time series data and preprocessed them in order to make them clean for use as train and target data. After such preprocessing, the price time series data were systematically encoded into the three-dimensional price tensor representing the past price changes of cryptocurrencies. We also presented our LSTM model structure as well as how to use such price tensor as input data of the LSTM model. In particular, a grid search-based k-fold cross-validation technique was applied to find the most suitable LSTM model parameters. Lastly, through the comparison of the f1-score values, our study showed that the LSTM model outperforms the gradient boosting model, a general machine learning model known to have relatively good prediction performance, for the time series classification of the cryptocurrency price trend. With the LSTM model, we got a performance improvement of about 7% compared to using the GB model.

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Cite this article
IEEE Style
D. Kwon, J. Kim, J. Heo, C. Kim and Y. Han, "Time Series Classification of Cryptocurrency Price Trend Based on a Recurrent LSTM Neural Network," Journal of Information Processing Systems, vol. 15, no. 3, pp. 694~706, 2019. DOI: 10.3745/JIPS.03.0120.

ACM Style
Do-Hyung Kwon, Ju-Bong Kim, Ju-Sung Heo, Chan-Myung Kim, and Youn-Hee Han. 2019. Time Series Classification of Cryptocurrency Price Trend Based on a Recurrent LSTM Neural Network, Journal of Information Processing Systems, 15, 3, (2019), 694~706. DOI: 10.3745/JIPS.03.0120.