AI 中文总结
本研究提出带InceptionTCN模块的多尺度TCN，结合利润优化阈值，利用2018-2025年数据预测比特币7天内涨超5%的走势，在5个基准模型中表现最优，可辅助投资者决策。
AI 中文摘要
比特币的未来波动是投资和风险管理中的重要关注点，投资者与金融机构需要准确预测这些价格走势以进行对冲和优化投资组合。本研究旨在回答比特币价格是否会在未来7天内上涨超过5%的问题，利用2018年2月至2025年12月的链上、市场及情绪数据开展分析。所提出的模型包含带InceptionTCN模块的多尺度时间卷积网络、CNN通道注意力、自适应平均池化以及成对排序损失，采用带瓶颈与融合层的扩张卷积，可高效捕捉1至4天时间跨度内的特征。考虑到数据集的类别不平衡问题，本研究采用AUC而非准确率等分类指标以更准确反映模型性能，同时应用利润优化的决策阈值，使模型选择与财务目标保持一致。将所提模型与ImprovedTCN_GRU、LSTM、TCN、XGBoost、随机森林共5个基准模型对比，结果显示该模型的AUC为0.6316，利润为1.703，优于所有基准模型，使用新型深度学习模型可帮助投资者做出更优的财务决策。
英文摘要
Bitcoin's future fluctuations are a substantial concern for investments and risk management. Investors and financial institutions require accurate forecasts of these price movements to hedge and optimize portfolios. This study aims to answer the question of whether Bitcoin's price will rise beyond 5% within the next 7 days by utilizing on-chain, market, and sentiment data from February 2018 to December 2025. The proposed model consists of a multi-scale temporal convolutional network with InceptionTCN blocks, CNN channel attention, adaptive average pooling, and a pairwise ranking loss. Dilated convolutions with bottleneck and fusion layers are employed to efficiently capture features over horizons from 1 to 4 days. Given the class imbalance in the dataset, AUC is used instead of accuracy and other classification metrics to reflect the model's performance better. Subsequently, a profit-optimized decision threshold is also applied to align model selection with financial objectives. The proposed model is compared with 5 other baselines: ImprovedTCN_GRU, LSTM, TCN, XGBoost, and Random Forest. Results indicate that the proposed model achieved an AUC of 0.6316 and a profit of 1.703, outperforming all baseline models. Using a novel deep learning model would assist investors in making better financial decisions.
