Classification of stochastic processes based on deep learning - Shamsan A Al-Murisi; Xiangong Tang; Weihua Deng (Journal of Physics: Complexity, 2024)
Stochastic processes model the time evolution of fluctuation phenomena widely observed in physics, chemistry, biology, and even social science. Typical examples include the dynamics of molecular interactions, cellular signalling, animal feeding, disease transmission, financial market fluctuation, and climate change. We create three datasets based on the codes obtained from the published article; the first one is for 12 stochastic processes, the second one for the Markov and non-Markov processes, and the third one for the Gaussian and non-Gaussian processes. We do the stochastic process classification by employing a series of convolution neural networks (CNNs), i.e. VGG16, VGG19, AlexNet, and MobileNetV2, achieving the accuracy rates of ‘99%’, ‘98%’, ‘95%’, and ‘94%’ on the first dataset, respectively; in the second dataset, the test accuracy of VGG16 is ‘100%’, and for the rest of the models, it is ‘99%’; and in the third dataset, the test accuracy of all models is ‘100%’, except the VGG19, which is ‘99%’. According to the findings, CNNs have slightly higher accuracy than classic feature-based approaches in the majority of circumstances, but at the cost of much longer training periods.