Solar filaments are cool, dark channels of partially-ionized plasma that lie above the chromosphere. Their structure follows the neutral line between local regions of opposite magnetic polarity. Previous research (e.g. Schmieder et al. 2013) has shown a positive correlation (80%) between the occurrence of filament eruptions and coronal mass ejections (CME’s). If certain filament properties, such as length, chirality, and tilt, indicate a tendency towards filament eruptions, one may be able to further predict an oncoming CME. Towards this end, we present a novel algorithm based on spatiotemporal analysis that systematically correlates filament eruptions documented in the Heliophysics Event Knowledgebase (HEK) with HEK filaments that have been grouped together using a tracking algorithm developed at Georgia State University (e.g. Kempton et al. 2014). We also find filament tracks that are not correlated with eruptions to form a null data set in a similar fashion. Finally, we compare the metadata from erupting and non-erupting filament tracks to discover which filament properties may present signs of an eruption onset. Through statistical methods such as the two-sample Kolmogorov-Smirnov test and Random Forest Classifier, we find that a filament that is increasing in length or changing in tilt with respect to the equator may be a useful gauge to predict a filament eruption. However, the average values of length and tilt for both datasets follow similar distributions, leading us to conclude that these parameters do not indicate an eruption event.