In this work, we study different combinations of the five time functions captured by a digitizer in presence or not of time variability. To this end, we propose two criteria independent of the classification step: Personal Entropy,introduced in our previous works and an intra-class variability measure based on Dynamic Time Warping. We confront both criteria to system performance using a Hidden Markov Model (HMM) and Dynamic Time Warping (DTW). Moreover, we introduce the concept of short-term time variability, proposed on MCYT-100, and long-term time variability studied with BIOMET database. Our experiments clarify conflicting results in the literature and confirm some other: pen inclination angles are very unstable in presence or not of time variability; the only combination which is robust to time variability is that containing only coordinates; finally, pen pressure is not recommended in the long-term context, although it may give better results in terms of performance (according to theclassifier used) in the short-term context