发表机构
Technical University of Berlin（柏林工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用LSTM神经网络基于价格和市值时间序列预测加密货币死亡，最佳ROC AUC达0.98，验证了时序特征的有效性，并论证了二分类优于多阶段生命周期模型。
AI 中文摘要
加密货币生态系统经历了非凡的增长，同时也伴随着同样显著的失败率，自2021年以来推出的所有代币中，超过52%到2025年初已停止交易。尽管这一现象规模巨大，但加密货币死亡的预测建模仍是一个发展不足的研究领域，受到定义模糊、数据稀缺以及缺乏细粒度生命周期框架的制约。本研究探讨了是否可以利用公开市场数据和深度学习方法来预测加密货币资产的失败。一个长短期记忆（LSTM）循环神经网络在82种加密货币资产（41种存活，41种已死）的90天每日参考价格和估计市值序列上进行了训练，数据来源于Coin Metrics，时间跨度为2020年至2026年。该模型使用严格的时间顺序训练-测试划分进行评估，以防止前视偏差。在最佳情况下，LSTM分类器在留出测试集上实现了0.98的受试者工作特征曲线下面积（ROC AUC）。最后，将该模型应用于未见数据，观察到ROC AUC降至0.59至0.65之间。研究结果表明，仅价格和市值中的时间模式就包含足够的判别信号，以识别正走向经济不活跃轨迹的资产。诊断分析显示，失败的资产在不活跃前的几个月内表现出逐渐的价值侵蚀和较高的波动性，而非突然的灾难性崩溃。本研究还记录了在当前数据条件下多阶段生命周期模型的实际不可行性，并论证了向二分类方法转变的合理性。
英文摘要
The cryptocurrency ecosystem has experienced extraordinary growth alongside an equally remarkable rate of failure, with over 52 percent of all tokens launched since 2021 ceasing to trade by early 2025. Despite the scale of this phenomenon, predictive modeling of cryptocurrency death remains an underdeveloped area of research, constrained by definitional ambiguity, data scarcity, and the absence of granular lifecycle frameworks. This work investigates whether the failure of cryptocurrency assets can be predicted using publicly available market data and deep learning methods. A Long Short-Term Memory (LSTM) recurrent neural network was trained on 90-day sequences of daily reference price and estimated market capitalization for 82 cryptocurrency assets (41 alive and 41 dead), sourced from Coin Metrics over the period 2020 to 2026. The model was evaluated using a strictly chronological train-test split to prevent look-ahead bias. The LSTM classifier achieved in best cases a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 0.98 on the held-out test set. Finally, the model was applied to unseen data, and it was observed that the ROC AUC decreased between 0.59 and 0.65. The findings demonstrate that temporal patterns in price and market capitalization alone contain sufficient discriminative signal to identify assets on a trajectory towards economic inactivity. Diagnostic analyzes reveal that failing assets exhibit gradual value erosion and elevated volatility in the months preceding inactivity, rather than sudden catastrophic collapse. This work also documents the practical infeasibility of a multi-stage lifecycle model under current data conditions and justifies the transition to a binary classification approach.
Commentsaccepted for 2026 8th International Conference on Blockchain Computing and Applications (BCCA), 16.11.-20.11.2026
