融合多源数据可显著提升加密货币预测准确率
From On-chain to Macro: Assessing the Importance of Data Source Diversity in Cryptocurrency Market Forecasting
- 整合技术指标、链上数据、情绪热度等多类数据
- 链上数据对短长期预测均最关键，宏观数据影响长期趋势
- 新特征筛选算法提升模型鲁棒性，适合量化交易研究者
本研究通过整合技术指标、链上数据、情绪与关注度指标、传统市场指数及宏观经济指标等多类数据，探究数据来源多样性对加密货币预测模型性能的影响。提出Crypto100指数（市值前100的加密货币）并设计新型特征降维算法，识别出最具影响力且稳定的特征。实验表明，多样化数据显著提升不同时间尺度下的预测表现：链上数据在短期与长期预测中均具决定性作用；传统市场指数与宏观经济指标对长期预测日益重要；综合多源数据使模型准确率大幅提升。研究揭示了加密市场短长期驱动因素，为构建更精准、稳健的预测模型奠定基础。
原文摘要 · Abstract (English)
This study investigates the impact of data source diversity on the performance of cryptocurrency forecasting models by integrating various data categories, including technical indicators, on-chain metrics, sentiment and interest metrics, traditional market indices, and macroeconomic indicators. We introduce the Crypto100 index, representing the top 100 cryptocurrencies by market capitalization, and propose a novel feature reduction algorithm to identify the most impactful and resilient features from diverse data sources. Our comprehensive experiments demonstrate that data source diversity significantly enhances the predictive performance of forecasting models across different time horizons. Key findings include the paramount importance of on-chain metrics for both short-term and long-term predictions, the growing relevance of traditional market indices and macroeconomic indicators for longer-term forecasts, and substantial improvements in model accuracy when diverse data sources are utilized. These insights help demystify the short-term and long-term driving factors of the cryptocurrency market and lay the groundwork for developing more accurate and resilient forecasting models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。