A convolutional framework for detecting event-driven dynamics in energy price series

2026-08-31

AI Summary

本文提出了一种卷积神经网络框架，用于检测能源价格序列中的事件驱动动态，并通过仿真和实际应用验证了其有效性。

Abstract

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.

Problem

• event-driven dynamics
• univariate time series
• energy price series
• convolutional neural network

Innovation

• convolutional neural network
• event-driven dynamics
• energy price series
• hierarchical CNN
• geopolitical dynamics