Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information

21 Jun 2019Maya OkawaTomoharu IwataTakeshi KurashimaYusuke TanakaHiroyuki TodaNaonori Ueda

Predicting when and where events will occur in cities, like taxi pick-ups, crimes, and vehicle collisions, is a challenging and important problem with many applications in fields such as urban planning, transportation optimization and location-based marketing. Though many point processes have been proposed to model events in a continuous spatio-temporal space, none of them allow for the consideration of the rich contextual factors that affect event occurrence, such as weather, social activities, geographical characteristics, and traffic... (read more)

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