Open Access
Research Article
A hybrid deep learning model for accurate time series forecasting of cryptocurrencies
*Aditya NagdiyaCorresponding authoraditya22.ad@gmail.comDepartment of Information Technology Institute of Engineering & Technology Devi Ahilya Vishwavidyalaya Indore, Madhya Pradesh, IndiaView full profile → , Vivek Kapoorvkapoor@ietdavv.edu.inDepartment of Information Technology Institute of Engineering & Technology Devi Ahilya Vishwavidyalaya Department of Information Technology Institute of Engineering and Technology Devi Ahilya UniversityIndore, Madhya Pradesh, 452017, IndiaView full profile → , Vrinda Tokekarvrindatokekar@yahoo.comDepartment of Information Technology Institute of Engineering & Technology Devi Ahilya VishwavidyalayaIndore, Madhya Pradesh, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 29 May 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1691
- Pages:
- 1061–1072
Abstract
A growing number of people and organizations are choosing to invest in cryptocurrencies. The development of precise forecasting models for cryptocurrencies is crucial due to their very volatile market. Financial forecasting has long made use of time series analysis and prediction, but conventional time series analysis techniques have trouble capturing intricate patterns and nonlinear relationships. On the other hand, although deep learning models show promise in time series analysis, their effectiveness depends on large amounts of data, which might result in overfitting. To predict Bitcoin prices, this research presents a hybrid model that combines long short-term memory (LSTM) and convolutional neural networks (CNN), where the CNN extracts features from the time series data, while the LSTM captures persistent patterns over time.
Keywords
Subject Classifications
Primary 93A30Secondary 49K15
References
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