Enhancing Bitcoin Log-return Prediction through TimesNet and Sentiment Analysis Integration

Abstract

In the realm of cryptocurrency forecasting, accurately predicting short-term Bitcoin log returns remains a challenging endeavor due to its inherent volatility and sensitivity to multifarious external factors. This study addresses this challenge by proposing an integrated approach that combines the capabilities of the TimesNet deep learning model with sentiment analysis techniques. TimesNet, specifically designed for time series data, has demonstrated proficiency in extracting salient patterns. When synergized with sentiment analysis, a more nuanced understanding of price determinants emerges. Preliminary results from our experiments indicate a significant enhancement in predictive accuracy within the Bitcoin market. Such advancements not only furnish investors and researchers with refined forecasting tools but also accentuate the burgeoning role of deep learning methodologies in the domain of financial forecasting.