Predictions of Cryptocurrency Prices Based on Inherent Interrelationships

DOI

10.2991/aebmr.k.220307.309How to use a DOI?

Keywords

cryptocurrency price prediction; interrelationships; machine learning

Abstract

The price of cryptocurrencies is predicted in this paper based on their intrinsic interrelationship with Bitcoin. The Kaggle dataset is gathered, standardized, collated, and extracted. Convolutional Neural Network (CNN) is compared to other machine learning methods such as Linear Regression and K-Nearest Neighbor (KNN), and then parameter optimization is performed. The empirical results show that Linear Regression is less accurate than the other two models, whereas the CNN model employing end-to-end solutions outperforms other models with the best accuracy (overall above 0.95) forecasting the price quantitatively and directly of the majority of cryptocurrencies, despite the fact that forecasting takes a long time and tweaking its parameters is extremely time-consuming. This paper proposes using research object interrelationships rather than extrinsic relationships.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Zhenyuan Wu
PY  - 2022
DA  - 2022/03/26
TI  - Predictions of Cryptocurrency Prices Based on Inherent Interrelationships
BT  - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
PB  - Atlantis Press
SP  - 1877
EP  - 1883
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220307.309
DO  - 10.2991/aebmr.k.220307.309
ID  - Wu2022
ER  -