Support Vector Regression to Improve  Ethereum Price Prediction for Trading Strategies

Muhamad Abdul Fatah; Martanto; Arif Rinaldi Dikananda; Ahmad Rifai

doi:10.59934/jaiea.v4i2.740

Authors

Muhamad Abdul Fatah Informatics Enginneering, STMIK IKMI Cirebon, Indonesia
Martanto Informatics Management, STMIK IKMI Cirebon, Indonesia
Arif Rinaldi Dikananda Software Engineering, STMIK IKMI Cirebon, Indonesia
Ahmad Rifai Informatics Enginneering, STMIK IKMI Cirebon, Indonesia

DOI:

https://doi.org/10.59934/jaiea.v4i2.740

Keywords:

Cryptocurrency, Ethereum, Support Vector Regression

Abstract

Predicting erratic assets like Ethereum is difficult in the dynamic cryptocurrency market. This study uses an enhanced Support Vector Regression (SVR) algorithm to create a daily price prediction model for Ethereum. Yahoo Finance provided the data, which was preprocessed to include missing value cleaning, normalization, and feature extraction of Moving Average (MA) and Exponential Moving Average (EMA). The data was collected between August 4, 2019 and August 4, 2024. An ideal combination was obtained by parameter optimization with GridSearchCV: gamma scale, linear kernel, epsilon of 1, and C of 100. The model performed well, as evidenced by its R2 of 0.9985 and MSE of 2137.97. The model's reliability in predicting Ethereum's price movement patterns was validated via prediction graphs. A 30-day forecast indicated a stable trend, with prices slightly decreasing from $2921.31 on January 1, 2025, to $2919.83 on January 31, 2025. These results highlight the importance of data preprocessing and parameter optimization in enhancing SVR model performance.

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