发表机构
Shanghai University of Finance and Economics; London School of Economics（上海财经大学; 伦敦政治经济学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种CNN框架，可检测单变量时间序列的事件驱动动态，其性能优于或相当于基于单个统计量的分类器，还能区分能源价格序列中不同类型的事件驱动动态。
AI 中文摘要
本文开发了一种通用的卷积神经网络（CNN）框架，用于检测单变量时间序列窗口中的异质性事件驱动动态。研究表明，所提出的CNN类别恰好代表基于区间、最大上涨幅度、最大下跌幅度和斜率变化的分类器，且在紧致域上可一致近似已实现波动率和自回归爆炸性。我们进一步建立了有限样本下代表性规则的误差界，以及跨规则学习的 oracle 不等式。模拟结果显示，随着训练样本量增长，所提模型的性能可与基于单个统计量的分类器相当或更优。在对6个每日能源价格序列的应用中，分层CNN可区分事件窗口与事件族；将拟合模型不经重新训练应用于2026年2月20日之后保留的观测值，可识别出2026年伊朗战争爆发前后多个石油及成品油序列中主要为地缘政治驱动的动态，同时区分出与天气相关的同期天然气价格峰值。
英文摘要
This paper develops a general convolutional neural network (CNN) framework for detecting heterogeneous event-driven dynamics in univariate time series windows. We show that the induced CNN class exactly represents classifiers based on range, maximum drawup, maximum drawdown and slope change, and uniformly approximates realised volatility and autoregressive explosiveness on compact domains. We further establish error bounds for representative rules in finite samples and an oracle inequality for learning across them. Simulations show that the proposed model can match or outperform classifiers based on individual statistics as the training sample grows. In an application to six daily energy price series, a hierarchical CNN distinguishes event windows and event families. Applied without retraining to observations withheld after 20 February 2026, the fitted model identifies predominantly geopolitical dynamics in several oil and refined product series around the outbreak of the 2026 Iran war, while distinguishing a contemporaneous natural gas spike associated with weather.
