Comparison of Performance of K-Nearest Neighbors and Neural Network Algorithm in Bitcoin Price Prediction

Sinkron Pub Date : 2024-03-31 DOI:10.33395/sinkron.v8i2.13418

Eko Aziz Apriadi, S. Sriyanto, Sri Lestari, Suhendro Yusuf Irianto

引用次数: 0

Abstract

This research evaluates and compares the performance of two prediction methods, namely K-Nearest Neighbors (K-NN) and Neural Network, in the context of Bitcoin price prediction. Historical Bitcoin price data is used as input to train and test both algorithms. Experimental results show that the K-NN algorithm produces a Root Mean Square Error (RSME) of 389,770 and a Mean Absolute Error (MAE) of 89,261, while the Neural Network has an RSME of 614,825 and an MAE of 284,190. Performance comparison analysis shows that, on this dataset, K-NN has better performance in predicting Bitcoin prices compared to Neural Network. These findings provide important insights for the selection of crypto asset price prediction models, especially Bitcoin, in financial and investment environments

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K-Nearest Neighbors 算法和神经网络算法在比特币价格预测中的性能比较

本研究评估并比较了两种预测方法（即 K-Nearest Neighbors (K-NN) 和神经网络）在比特币价格预测方面的性能。比特币的历史价格数据被用作这两种算法的训练和测试输入。实验结果显示，K-NN 算法的均方根误差（RSME）为 389,770，平均绝对误差（MAE）为 89,261，而神经网络的均方根误差（RSME）为 614,825，平均绝对误差（MAE）为 284,190。性能比较分析表明，在该数据集上，K-NN 在预测比特币价格方面的性能优于神经网络。这些发现为在金融和投资环境中选择加密资产（尤其是比特币）价格预测模型提供了重要启示。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sinkron

自引率

0.00%

发文量

204

审稿时长

4 weeks