Efficient Machine-Learning-Based Crypto Forecasting Analysis

Sidra Hussain, Sheikh Mim, Doina Logofatu

2023

Abstract

Cryptocurrency is the current evolving financial market that gives scope for many researchers and machine learning assets to be put into production. Cryptocurrency forecasting is a challenge as following financial assets is difficult due to their volatility and unscalable factor dependencies. This paper puts forward the data of fourteen different types of cryptocurrencies, which will be used to build machine learning (ML) models to forecast the crypto scores in the next fiscal year. For this, four different models are used, and their performance is evaluated and tested. The models are Linear Regression, Decision Tree, Random Forest, and Gradient Boosting. The results obtained from the models show a perfect fit and good hyperparameter tuning, giving evidence of good feature engineering and data scraping. Overall, the models are meant to benefit the financial market immensely by helping to forecast and help investments build in sales and purchasing.

Paper Citation

in Harvard Style

Hussain S., Mim S. and Logofatu D. (2023). Efficient Machine-Learning-Based Crypto Forecasting Analysis. In Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH; ISBN 978-989-758-668-2, SciTePress, pages 352-360. DOI: 10.5220/0012117800003546

in Bibtex Style

@conference{simultech23,
author={Sidra Hussain and Sheikh Mim and Doina Logofatu},
title={Efficient Machine-Learning-Based Crypto Forecasting Analysis},
booktitle={Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH},
year={2023},
pages={352-360},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012117800003546},
isbn={978-989-758-668-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH
TI - Efficient Machine-Learning-Based Crypto Forecasting Analysis
SN - 978-989-758-668-2
AU - Hussain S.
AU - Mim S.
AU - Logofatu D.
PY - 2023
SP - 352
EP - 360
DO - 10.5220/0012117800003546
PB - SciTePress