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

Abstract

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.