Bayesian Opt. GPs vs Linear models and Neural Networks for parameter optimizations [R]

Hi,

Relatively new to deep learning. I wanted some opinions on which of these approaches might be best for time series data and spectral analysis. I currently use a GP and it works pretty well, but I’m wondering what the computational tradeoffs and so forth might be. Any ideas?