  Social coding platforms, such as GitHub, can serve as natural laboratories
for studying the diffusion of innovation through tracking the pattern of code
adoption by programmers. This paper focuses on the problem of predicting the
popularity of software repositories over time; our aim is to forecast the time
series of popularity-related events (code forks and watches). In particular, we
are interested in cross-repository patterns-how do events on one repository
affect other repositories? Our proposed LSTM (Long Short-Term Memory) recurrent
neural network integrates events across multiple active repositories,
outperforming a standard ARIMA (Auto-Regressive Integrated Moving Average) time
series prediction based on the single repository. The ability of the LSTM to
leverage cross-repository information gives it a significant edge over standard
time series forecasting.
