We consider different statistical models for the call arrival process in telephone call centers. We evaluate the forecasting accuracy of those models by describing results from an empirical study analyzing real-life call center data. We test forecasting accuracy using different lead times, ranging from weeks to hours in advance, to mimic real-life challenges faced by call center managers. The models considered are: (i) a benchmark fixed-effects model which does not exploit any dependence structures in the data; (ii) a mixed-effects model which takes into account both interday (day-to-day) and intraday (within day) correlations; (iii) two new bivariate mixed-effects models, for the joint distribution of the arrival counts to two separate queues, which exploit correlations between different call types. Our study shows the importance of accounting for different correlation structures in the data.