The goal of this study is to characterize the accuracy of prediction of physiological responses for varying forecast lengths using multi-modal data streams from wearable health monitoring platforms. We specifically focus on predicting breathing rate due to its significance in medical and exercise physiology research. We implement a nonlinear support vector machine regression model for accurate prediction of future values of these physiological signals with forecast windows of up to one minute long. We explore the effects of heart rate and various other sensing modalities in prediction of breathing rate. Results reveal that including other physiological responses and activity information captured by inertial measurements in the regression model improves the breathing rate prediction accuracy. We carried out experiments by collecting and analyzing physiological and activity data outside the lab using a wearable platform composed of various off-the-shelf sensors.