A technology is provided for predicting congestion or crowding of services over a future time interval, and may be utilized for forecasting congestion in a hospital emergency department. One embodiment of this technology comprises a decision support tool for resources management to prevent overcrowding and long waiting times, or for mitigating ED congestion by, for example, warning hospital managers that a significant likelihood exists of ED congestion over a future time frame, or automatically initiating mitigative actions. A time series of consecutive ED arrivals timestamps is processed to determine a presence (or absence) of positive autocorrelation or self-similarity and estimate Hurst exponent values to generate a forecast model. The forecast model is utilized to determine future ED demand.