COTN: A Chaotic Oscillatory Transformer Network for Complex Volatile Systems under Extreme Conditions

• URL: http://arxiv.org/abs/2511.06273v1
• Date: Sun, 09 Nov 2025 08:17:19 GMT
• Title: COTN: A Chaotic Oscillatory Transformer Network for Complex Volatile Systems under Extreme Conditions
• Authors: Boyan Tang, Yilong Zeng, Xuanhao Ren, Peng Xiao, Yuhan Zhao, Raymond Lee, Jianghua Wu,
• Abstract summary: Accurate prediction of financial and electricity markets, especially under extreme conditions, remains a significant challenge.<n>We propose the Chaoticy Transformer Network (COTN), which combines a Transformer architecture with a novel Lee activation function.<n>COTN incorporates an Autoencoder Self-Regressive (ASR) module to detect and isolate abnormal market patterns.<n>Our approach outperforms state-of-the-art deep learning models like Informer by up to 17% and traditional statistical methods like GARCH by as much as 17%.
• Score: 4.606846373731374
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate prediction of financial and electricity markets, especially under extreme conditions, remains a significant challenge due to their intrinsic nonlinearity, rapid fluctuations, and chaotic patterns. To address these limitations, we propose the Chaotic Oscillatory Transformer Network (COTN). COTN innovatively combines a Transformer architecture with a novel Lee Oscillator activation function, processed through Max-over-Time pooling and a lambda-gating mechanism. This design is specifically tailored to effectively capture chaotic dynamics and improve responsiveness during periods of heightened volatility, where conventional activation functions (e.g., ReLU, GELU) tend to saturate. Furthermore, COTN incorporates an Autoencoder Self-Regressive (ASR) module to detect and isolate abnormal market patterns, such as sudden price spikes or crashes, thereby preventing corruption of the core prediction process and enhancing robustness. Extensive experiments across electricity spot markets and financial markets demonstrate the practical applicability and resilience of COTN. Our approach outperforms state-of-the-art deep learning models like Informer by up to 17% and traditional statistical methods like GARCH by as much as 40%. These results underscore COTN's effectiveness in navigating real-world market uncertainty and complexity, offering a powerful tool for forecasting highly volatile systems under duress.

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