DDN-GP: Estimating Regression Predictive Distributions with Missing Data

Probability density estimation in time series often encoun- ters missing values, which compromise data completeness and usability, making it difficult to accurately estimate distributions and leading to biased results. To address this challenge, we propose a novel probabil- ity density estimation method called DDN-GP, which introduces Gaus- sian Process GP to Deconvolutional Density Networks DDN . This ap- proach uses a nonlinear dimensionality reduction approach, employing GP in the latent space to handle missing input data, and takes advan- tage of DDN to estimate arbitrarily distributed times in time series even with missing output data, ultimately improving both prediction accu- racy and model robustness. We validate DDN-GP on multiple datasets with missing data, and the experimental results demonstrate that our approach enhances predictive performance quantification compared to existing methods.