Forecasting Time Series in Healthcare With Gaussian Processes and Dynamic Time Warping Based Subset Selection KU Leuven Ghent University
Modelling real-world time series can be challenging in the absence of sufficient data. Limited data in healthcare, can arise for several reasons, namely when the number of subjects is insufficient or the observed time series is irregularly sampled at a very low sampling frequency. This is especially true when attempting to develop personalised models, as there are typically few data points available for training from an individual subject. ...