Multi-step Prediction with Missing Smart Sensor Data using Multi-task Gaussian Processes

With the proliferation of sensors and the increased connectivity of citizens, many global cities are increasingly adopting Smart City initiatives. Such initiatives provide real-time monitoring capabilities, and effective modelling techniques allow the prediction of future states in a city. For example, urban electricity smart meter data can be utilised to predict future demand in order to facilitate capacity planning. However, the accuracy of this foresight is often marred by low quality and missing sensor data in real-world systems. In this work, we focus on the problem of reliable forecasting by mitigating the effect of missing data on forecast accuracy. In order to mitigate the effects of missing data, we develop a multi-task learning scheme to jointly learn Gaussian Process Regression models between highly correlated sensors. We demonstrate that our methods are robust in a variety of error generation scenarios. We validate our methods based on publicly available and real-world datasets related to electricity smart meters in a university campus and pedestrian counts in a global city, where we achieve significant improvement over competitive baselines and other effective forecasting methods.