Efficient CNN-LSTM based Parameter Estimation of Levy Driven Stochastic Differential Equations

arXiv:2403.04246v1 Announce Type: cross
Abstract: This study addresses the challenges in parameter estimation of stochastic differential equations driven by non-Gaussian noises, which are critical in understanding dynamic phenomena such as price fluctuations and the spread of infectious diseases. Previous research highlighted the potential of LSTM networks in estimating parameters of alpha stable Levy driven SDEs but faced limitations including high time complexity and constraints of the LSTM chaining property. To mitigate these issues, we introduce the PEnet, a novel CNN-LSTM-based …

abstract arxiv challenges cnn cs.ai cs.lg differential diseases dynamic infectious diseases lstm networks parameters price research stat.ml stochastic study type understanding