Enhanced Interpretable Forecasting of Cryptocurrency Prices Using Autoencoder Features and a Hybrid CNN-LSTM Model

Enhanced Interpretable Forecasting of Cryptocurrency Prices Using Autoencoder Features and a Hybrid CNN-LSTM Model

Predicting the price of Bitcoin is crucial, primarily because of the market’s rapid volatility and non-linear environment. For enhanced prediction of the price of Bitcoin, this research proposed a novel interpretable hybrid technique that combines long short-term memory (LSTM) networks with convolut...

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Bibliographic Details
Main Authors: Wajeeha Badar, Shabana Ramzan, Ali Raza, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Mathematics
Subjects:
bitcoin price prediction
hybrid model
CNN
LSTM
variational autoencoders
SHAP
Online Access:https://www.mdpi.com/2227-7390/13/12/1908
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https://www.mdpi.com/2227-7390/13/12/1908

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