This work studies the impact of using dynamic information as features in a machine learning algorithm for the prediction task of classifying critically ill patients in two classes ...
Short, high-dimensional, Multivariate Time Series (MTS) data are common in many fields such as medicine, finance and science, and any advance in modelling this kind of data would b...
Paul Kellam, Xiaohui Liu, Nigel J. Martin, Christi...
Many genomic sequences and, more generally, (multivariate) time series display tremendous variability. However, often it is reasonable to assume that the sequence is actually gene...
This paper introduces an approach for closer integration of selforganizing maps into the visualization of spatio-temporal phenomena in GIS. It is proposed to provide a more explic...
In functional Magnetic Resonance Imaging (fMRI) data analysis, normalization of time series is an important and sometimes necessary preprocessing step in many widely used methods. ...
Jian Cheng, Feng Shi, Kun Wang, Ming Song, Jiefeng...