GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models [Technical Report]

• URL: http://arxiv.org/abs/2201.03244v1
• Date: Mon, 10 Jan 2022 09:59:40 GMT
• Title: GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models [Technical Report]
• Authors: Jiabao Jin, Peng Cheng, Lei Chen, Xuemin Lin, Wenjie Zhang
• Abstract summary: We study a region's optimal grid size selection problem (OGSS) We analyze the upper bound of real errortemporal prediction models. We propose two algorithms, namely Search and Iterative Method, to automatically find the optimal grid size.
• Score: 43.23981661006877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of traffic prediction technology, spatiotemporal prediction models have attracted more and more attention from academia communities and industry. However, most existing researches focus on reducing model's prediction error but ignore the error caused by the uneven distribution of spatial events within a region. In this paper, we study a region partitioning problem, namely optimal grid size selection problem (OGSS), which aims to minimize the real error of spatiotemporal prediction models by selecting the optimal grid size. In order to solve OGSS, we analyze the upper bound of real error of spatiotemporal prediction models and minimize the real error by minimizing its upper bound. Through in-depth analysis, we find that the upper bound of real error will decrease then increase when the number of model grids increase from 1 to the maximum allowed value. Then, we propose two algorithms, namely Ternary Search and Iterative Method, to automatically find the optimal grid size. Finally, the experiments verify that the error of prediction has the same trend as its upper bound, and the change trend of the upper bound of real error with respect to the increase of the number of model grids will decrease then increase. Meanwhile, in a case study, by selecting the optimal grid size, the order dispatching results of a state-of-the-art prediction-based algorithm can be improved up to 13.6%, which shows the effectiveness of our methods on tuning the region partition for spatiotemporal prediction models.

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