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Occupancy detection capabilities provided by modern connected thermostats enable adaptive thermal control of residential buildings. While this adaptation might simply consider the current occupancy state, a more proactive optimized system could also consider the probability of future occupancy in order to balance comfort and energy savings. Because such proactive control relies on accurate occupancy prediction, we comparatively evaluate a number of machine learning models for predicting measurements of the future occupancy state of homes that is critically enabled by thermostat data from real households in ecobee's Donate Your Data program. We consider a variety of models including simple heuristic and historical average baselines, traditional machine learning classifiers, and sequential models commonly used for time series prediction. We evaluate the performance of each model according to …