Spatio-temporal Predictive Queries encompass a spatio-temporal constraint, defining a region, a target variable, and an evaluation metric. The output of such queries presents the future values for the target variable computed by predictive models at each point of the spatio-temporal region. Unfortunately, especially for large spatio-temporal domains with millions of points, training temporal models at each spatial domain point is prohibitive. In this work, we propose a data-driven approach for selecting pre-trained temporal models to be applied at each query point. The chosen approach applies a model to a point according to the training and input time series similarity. The approach avoids training a different model for each domain point, saving model training time. Moreover, it provides a technique to decide on the best-trained model to be applied to a point for prediction. In order to assess the applicability of the proposed strategy, we evaluate a case study for temperature forecasting using historical data and auto-regressive models. Computational experiments show that the proposed approach, compared to the baseline, achieves equivalent predictive performance using a composition of pre-trained models at a fraction of the total computational cost.
Recent works [5, 3, 11], highlight some of the challenges encountered in predictive serving systems. 2We are interested in the problem arising in the presence of multiple competing predictive models. 3This may be the case in large companies in which autonomously developed models about the same 4 phenomenon (a.k.a competing models) are deployed and used during an ad-hoc predictive query. In 5 this context, the training and validation processes used in building competing models may consider 6 different fragments of the modeled phenomenon domain leading to a variation on their predictive 7
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