Predicting Longitudinal User Activity at Fine Time Granularity in Online Collaborative Platforms

This paper introduces a decomposition approach to address the problem of predicting different user activities at hour granularity over a long period of time. Our approach involves two steps. First, we used a temporal neural network ensemble to predict the number of each type of activity that occurred in a day. Second, we used a set of neural networks to assign the events to a user-repository pair in a particular hour. We focused this work on a subset of the public GitHub dataset that records the activities of over 2 million users on over 400,000 software repositories. Our experiments show we were able to predict hourly user-repo activity with reasonably low error. Our simulations are accurate for 1-3 weeks (168-504 hours) after inception, with accuracy gradually falling off. It was shown that activity on Twitter and Reddit increases the accuracy of activity prediction on GitHub for most events.