A complexity gap computed as the normalized largest eigenvalue minus average pairwise correlation collapses to zero during shocks and shows a false-recovery phase before true restoration, predicting higher future volatility when low.
Impact of data normalization on deep neural network for time series forecasting
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Quantum reservoir computing with distributed architectures reduces time-series forecasting errors by up to 78.8% MAE and 72.3% RMSE in NISQ simulations compared to classical methods.
citing papers explorer
-
Structural Dynamics of G5 Stock Markets During Exogenous Shocks: A Random Matrix Theory-Based Complexity Gap Approach
A complexity gap computed as the normalized largest eigenvalue minus average pairwise correlation collapses to zero during shocks and shows a false-recovery phase before true restoration, predicting higher future volatility when low.
-
Scalable Quantum Reservoir Computing over Distributed Quantum Architectures
Quantum reservoir computing with distributed architectures reduces time-series forecasting errors by up to 78.8% MAE and 72.3% RMSE in NISQ simulations compared to classical methods.