doi: 10.18178/ijml.2024.14.3.1166
The Effect of Long Short-Term Memory Forecasting with Varied Time Frames
Pongsakorn Teerarassamee1
,
Ratiporn Chanklan1 ,
Kittisak Kerdprasop1 ,
Nittaya Kerdprasop1,*
- 1School of Computer Engineering, Suranaree University of Technology, Thailand.
- Manuscript receivedDecember 26, 2023
- revisedMarch 4, 2024
- acceptedMarch 17, 2024
- publishedSeptember 26, 2024
Abstract
This study explores the application of Long Short-Term Memory (LSTM) networks to predict the price of Bitcoin over various time periods. The performance of the model is evaluated using cross-validation methods and parameter selection techniques. The results show that the LSTM model is able to accurately predict the price of Bitcoin, with performance improving as the time period of the data increases. This suggests that LSTM networks are well-suited for modeling time series. Our research study contributes to the determination of the optimal parameters and cross-validation methods for LSTM models applied to financial market data.
Keywords
- bitcoin forecasting
- long short-term memory
- LSTM
- deep learning
- machine learning
How to Cite
Pongsakorn Teerarassamee, Ratiporn Chanklan, Kittisak Kerdprasop, and Nittaya Kerdprasop, "The Effect of Long Short-Term Memory Forecasting with Varied Time Frames," International Journal of Machine Learning, vol. 14, no. 3, pp. 107-112, 2024. https://doi.org/10.18178/ijml.2024.14.3.1166
Copyright & License
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).