Gold and Bitcoin Price Prediction based on KNN, XGBoost and LightGBM Model
DOI:
https://doi.org/10.54097/hset.v39i.6635Keywords:
Machine Learning; Price Prediction; KNN Regression; XGBoost; LightGBM.Abstract
In the past few decades, there has been an increasing demand for assets trading with help of machine learning. Contemporarily, the cryptocurrency and gold market has become prosperous with extremely dramatical fluctuations. This paper aims to study the trading price laws based on machine learning scenarios of Bitcoin and Gold to predict the price of the two currencies. To be specific, this study gives an inside view of the application of a method combined three algorithms (i.e., KNN, XGBoost and LightGBM) to predict the future Gold and Bitcoin price browser based on past data from 2017 to 2022. According to the analysis, the study shows the difference of three models, the accuracy of the combined algorithms and proves the related metrics to predict the price of the Gold and Bitcoin. Overall, these results give a guideline for the investor to make sensible decisions about Bitcoin and Gold price and shed light on guiding further exploration of price forecasting in terms of machine learning approaches.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
