  This paper presents VAEneu, an innovative autoregressive method for multistep
ahead univariate probabilistic time series forecasting. We employ the
conditional VAE framework and optimize the lower bound of the predictive
distribution likelihood function by adopting the Continuous Ranked Probability
Score (CRPS), a strictly proper scoring rule, as the loss function. This novel
pipeline results in forecasting sharp and well-calibrated predictive
distribution. Through a comprehensive empirical study, VAEneu is rigorously
benchmarked against 12 baseline models across 12 datasets. The results
unequivocally demonstrate VAEneu's remarkable forecasting performance. VAEneu
provides a valuable tool for quantifying future uncertainties, and our
extensive empirical study lays the foundation for future comparative studies
for univariate multistep ahead probabilistic forecasting.
