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Conference Papers Year : 2022

A Data-Driven Model Selection Approach to Spatio-Temporal Prediction

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Abstract

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.
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Dates and versions

lirmm-03798483 , version 1 (05-10-2022)

Identifiers

Cite

Rocío Zorrilla, Eduardo Ogasawara, Patrick Valduriez, Fabio Porto. A Data-Driven Model Selection Approach to Spatio-Temporal Prediction. SBBD 2022 - Simpósio Brasileiro de Banco de Dados, SBBD, Sep 2022, Buzios, Brazil. pp.1-12, ⟨10.5753/sbbd.2022.224638⟩. ⟨lirmm-03798483⟩
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