CryptoPulse: Short-Term Cryptocurrency Forecasting with Dual-Prediction and Cross-Correlated Market Indicators

Abstract

Cryptocurrencies fluctuate in markets with high price volatility, which becomes a great challenge for investors. To aid investors in making informed decisions, systems predicting cryptocurrency market movements have been developed, commonly framed as feature-driven regression problems that focus solely on historical patterns favored by domain experts. However, these methods overlook three critical factors that significantly influence the cryptocurrency market dynamics: 1) the macro investing environment, reflected in major cryptocurrency fluctuations, which can affect investors’ collaborative behaviors, 2) overall market sentiment, heavily influenced by news, which impacts investors’ strategies, and 3) technical indicators, which offer insights into overbought or oversold conditions, momentum, and market trends are often ignored despite their relevance in shaping short-term price movements. In this paper, we propose a dual prediction mechanism that enables the model to forecast the next day’s closing price by incorporating macroeconomic fluctuations, technical indicators, and individual cryptocurrency price changes. Furthermore, we introduce a novel refinement mechanism that enhances the prediction through market sentiment-based rescaling and fusion. In experiments, the proposed model achieves state-of-the-art performance (SOTA), consistently outperforming ten comparison methods in most cases. Our code and data can be found at https://github.com/aamitssharma07/SAL-Cryptopulse