The workload of air traffic controllers (ATCs) is increasing due to the growing air traffic. Early alarms of loss of separation (LoS) events between aircraft are critical for ATCs to coordinate intensive traffic safely. The authors studied the time series of traffic densities and numbers of turning aircraft in a given sky section as early indicators of pending LoS. Simulator experiment produced data for comparing the prediction accuracies of the logistic regression models generated from the time series of traffic densities and numbers of turning aircraft, and combinations of these two. We studied different sections of the time series to examine the possibility of early detection and found that 1) the regression model based on the traffic density time series is more accurate than the model using the numbers of turning aircraft; 2) properly combining sections of the time series could produce models that achieve earlier predictions without losing accuracy.