Predictive Modeling of Bitcoin Transaction: Daily Analysis

Dharshini G, Bhavadharani K, Dhivya Bharathi T, Arunkumar T

2025

Abstract

With the fast development of the cryptocurrency market, the accurate price prediction of cryptocurrency has become a fault finding for traders. However, because of the difficult and unpredictable nature of price movements which do not have a simple pattern. This makes it complex to examine sequential data points covered over a long period of time. The prediction of cryptocurrency by researchers has explored various approaches which includes Machine Learning (ML) and Deep Learning (DL) to forecast price movements. Factors such as sudden market shifts, external events and investor sentiment contribute to unpredictability. To forecast the price of bitcoin cryptocurrency with Prophet, Long short term memory (LSTM), gated recurrent neural networks to segment the data which consists of daily and half-hourly data transactions were used. In terms of evaluation metrics Mean Absolute Error (MAE), R-Squared, Mean squared error (MSE) were used.

Paper Citation

in Harvard Style

G D., K B., T D. and T A. (2025). Predictive Modeling of Bitcoin Transaction: Daily Analysis. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 173-180. DOI: 10.5220/0013611300004664

in Bibtex Style

@conference{incoft25,
author={Dharshini G and Bhavadharani K and Dhivya Bharathi T and Arunkumar T},
title={Predictive Modeling of Bitcoin Transaction: Daily Analysis},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={173-180},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013611300004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Predictive Modeling of Bitcoin Transaction: Daily Analysis
SN - 978-989-758-763-4
AU - G D.
AU - K B.
AU - T D.
AU - T A.
PY - 2025
SP - 173
EP - 180
DO - 10.5220/0013611300004664
PB - SciTePress