Image-Based Cryptocurrency Trend Prediction with Explainable Deep Learning

Nethmi Wijesinghe

Abstract: The rise of cryptocurrencies has reshaped financial markets, introducing assets that operate outside traditional banking systems. Yet, the market's volatility challenges traders who rely on manual technical analysis, which is both time-intensive and error-prone. This paper introduces CryptoVisionX, an advanced deep-learning framework designed for the automated prediction of cryptocurrency prices. Utilizing Convolutional Neural Networks (CNNs) for image feature extraction and Long Short-Term Memory (LSTM) networks for time series analysis, the model enhances prediction accuracy and efficiency. To combat data gaps, CryptoVisionX employs a comprehensive image-based dataset across various cryptocurrencies. Furthermore, it incorporates explainable AI (XAI) to increase the transparency of predictive models, fostering trust among users. The model's design navigates the trade-off between general applicability and precision, providing a scalable yet accurate forecasting tool. This research addresses key gaps in cryptocurrency analytics, offering a novel solution that could revolutionize trading strategies in the financial technology domain.