4 papers
Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation
Bo Tang, Weiwei Xie
Spiking Neural Networks (SNNs) provide a promising framework for energy-efficient and biologically grounded computation; however, scalable learning in deep recurrent architectures…
FCOC: A Fractal-Chaotic Co-driven Framework for Financial Volatility Forecasting
Yilong Zeng, Boyan Tang, Xuanhao Ren +3
This paper introduces the Fractal-Chaotic Oscillation Co-driven (FCOC) framework, a novel paradigm for financial volatility forecasting that systematically resolves the dual challe…
A Hybrid Autoencoder-Transformer Model for Robust Day-Ahead Electricity Price Forecasting under Extreme Conditions
Boyan Tang, Xuanhao Ren, Peng Xiao +3
Accurate day-ahead electricity price forecasting (DAEPF) is critical for the efficient operation of power systems, but extreme condition and market anomalies pose significant chall…
COTN: A Chaotic Oscillatory Transformer Network for Complex Volatile Systems under Extreme Conditions
Boyan Tang, Yilong Zeng, Xuanhao Ren +4
Accurate prediction of financial and electricity markets, especially under extreme conditions, remains a significant challenge due to their intrinsic nonlinearity, rapid fluctuatio…