Predicting the Close-price of Cryptocurrency Using the Kernel Regression Algorithm

The aim of this work is to utilize the kernel regression (KR) approach to predict the closed-price for cryptocurrencies. This study makes use of three datasets: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The min-max normalization method was used to scale feature values to a common range, oft...

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Bibliographic Details
Published in2023 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C) pp. 1 - 7
Main Authors Polpinij, Jantima, Namee, Khanista, Sibunruang, Chumsak, Chothanom, Aniruth, Khamket, Thananchai, Meny, Ajeej, Charoensak, Rungtip, Kaenampornpan, Manasawee, Luaphol, Bancha
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.08.2023
Subjects
Bitcoin
Closed-price cryptocurrency
kernel regression (KR)
long-short term memory (LSTM)
predictive model
Predictive models
Rendering (computer graphics)
Sensitivity
Supply and demand
Support vector machines
support vector regression (SVR)
Technological innovation
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DOI10.1109/RI2C60382.2023.10356032

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Summary:The aim of this work is to utilize the kernel regression (KR) approach to predict the closed-price for cryptocurrencies. This study makes use of three datasets: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The min-max normalization method was used to scale feature values to a common range, often between 0 and 1. Furthermore, support vector regression (SVR) and long-short term memory (LSTM) were used to compare the prediction model-based on KR. The result of the KR models utilizing RMSE and MAPE demonstrated that the predictive model-based on KR gave more satisfying results.
DOI:10.1109/RI2C60382.2023.10356032