  A multiple objective space-time forecasting approach is presented involving
cyclical curve log-regression, and multivariate time series spatial residual
correlation analysis. Specifically, the mean quadratic loss function is
minimized in the framework of trigonometric regression. While, in our
subsequent spatial residual correlation analysis, maximization of the
likelihood allows us to compute the posterior mode in a Bayesian multivariate
time series soft-data framework. The presented approach is applied to the
analysis of COVID-19 mortality in the first wave affecting the Spanish
Communities, since March, 8, 2020 until May, 13, 2020. An empirical comparative
study with Machine Learning (ML) regression, based on random k-fold
cross-validation, and bootstrapping confidence interval and probability density
estimation, is carried out. This empirical analysis also investigates the
performance of ML regression models in a hard- and soft- data frameworks. The
results could be extrapolated to other counts, countries, and posterior
COVID-19 waves.
