Investigating Hamiltonian Monte Carlo for Real-time Network Traffic Prediction

Hamiltonian Monte Carlo (HMC) has emerged as a promising method for predicting actual-time community visitors. HMC is a specialized form of Markov Chain Monte Carlo (MCMC) sampling and may reduce the computational complexity associated with MCMC, even offering green performance. In this paper, we present a radical investigation of the efficacy of HMC for community traffic prediction and compare it to the present MCMC techniques. We utilize three publicly available visitor datasets and examine the overall performance of the algorithms in terms of predictive accuracy, computational complexity, and computational pace. Our results show that HMC can outperform the present MCMC strategies in phrases of accuracy and speed, making it an appealing option for predicting real-time community visitors. In addition, we discover that HMC plays higher as the dimensions of the dataset increase, suggesting capacity scalability advantages.