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arXiv Machine Learning By Caixia Xu, Piotr Fryzlewicz

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

Read the original on arXiv Machine Learning →

The paper introduces a convolutional neural network framework for detecting event-driven dynamics in univariate time‑series windows, showing that it can represent classifiers based on range, maximum drawup, maximum drawdown, and slope change, and can uniformly approximate realised volatility and autoregressive explosiveness. It provides error bounds for representative rules in finite samples and an oracle inequality for learning across them, with simulations indicating that the model matches or outperforms individual‑statistic classifiers as training data increases. In an application to six daily energy price series, a hierarchical CNN identifies event windows and families, correctly detecting geopolitical dynamics around the 2026 Iran war and a natural gas spike linked to weather without retraining on post‑February 2026 data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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