Understanding the Association Between Cryptocurrency Price Predictive Performance and Input Features


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 1, pp. 19-28, Jan. 2022
https://doi.org/10.3745/KTSDE.2022.11.1.19,   PDF Download:
Keywords: LSTM, Deep Learning, Input Feature, Cryptocurrency, Price Prediction, Data Analysis
Abstract

Recently, cryptocurrency has attracted much attention, and price prediction studies of cryptocurrency have been actively conducted. Especially, efforts to improve the prediction performance by applying the deep learning model are continuing. LSTM (Long Short-Term Memory) model, which shows high performance in time series data among deep learning models, is applied in various views. However, it shows low performance in cryptocurrency price data with high volatility. Although, to solve this problem, new input features were found and study was conducted using them, there is a lack of study on input features that drop predictive performance. Thus, in this paper, we collect the recent trends of six cryptocurrencies including Bitcoin and Ethereum and analyze effects of input features on the cryptocurrency price predictive performance through statistics and deep learning. The results of the experiment showed that cryptocurrency price predictive performance the best when open price, high price, low price, volume and price were combined except for rate of closing price fluctuation.


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Cite this article
[IEEE Style]
J. Park and Y. Seo, "Understanding the Association Between Cryptocurrency Price Predictive Performance and Input Features," KIPS Transactions on Software and Data Engineering, vol. 11, no. 1, pp. 19-28, 2022. DOI: https://doi.org/10.3745/KTSDE.2022.11.1.19.

[ACM Style]
Jaehyun Park and Yeong-Seok Seo. 2022. Understanding the Association Between Cryptocurrency Price Predictive Performance and Input Features. KIPS Transactions on Software and Data Engineering, 11, 1, (2022), 19-28. DOI: https://doi.org/10.3745/KTSDE.2022.11.1.19.