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

• Published in: 2023 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
• Language: English
• 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
• DOI: 10.1109/RI2C60382.2023.10356032

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.