In supervised learning, on data collected over space and time, different relationships can be found over different spatiotemporal regions. In such situations, an appropriate spatiotemporal data partitioning followed by building specialized predictors could often achieve higher overall prediction accuracy than when learning a single predictor on all the data. In practice, partitions are typically decided based on prior knowledge. As an alternative to domain-based partitioning, we propose a method that automatically discovers a spatiotemporal partitioning through the competition of regression models. The method is evaluated on a challenging problem using satellite observations to predict Aerosol Optical Depth (AOD), which represents the amount of depletion that a beam of radiation undergoes as it passes through the atmosphere. Our experiments used more than 20,000 labeled data points collected during 3 years from more than 100 sites worldwide. Our partitioning-based approach was compared to the recently developed operational AOD prediction algorithm, called C5, which uses domain knowledge for spatiotemporal partitioning of the Earth and implements a region-specific deterministic predictor that utilizes forward simulations from the postulated physical models. Data partitioning used in C5 divides the world into three spatiotemporal regions that differ based on the location and the time of the year as decided by domain experts. The results showed that a neural network predictor trained on all the data has accuracy comparable to C5. When specialized neural network predictors were learned on C5-based partitions, the overall prediction accuracy was not improved. On the other hand, our competition-based spatiotemporal data partitioning approach resulted in large accuracy improvements. The most accurate results were obtained when (1) the data from each of the sites were split into two temporal subsets, one for winter-spring months and another for summer-fall months; and (2) two neural network predictors were competing for each of the identified spatiotemporal subsets.
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