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Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction
[article]
2021
arXiv
pre-print
Spatiotemporal prediction of event data is a challenging task with a long history of research. While recent work in spatiotemporal prediction has leveraged deep sequential models that substantially improve over classical approaches, these models are prone to overfitting when the observation is extremely sparse, as in the task of crime event prediction. To overcome these sparsity issues, we present Multi-axis Attentive Prediction for Sparse Event Data (MAPSED). We propose a purely attentional
arXiv:2110.01794v1
fatcat:j7xdt3w2lvdlnideciexc3ti2e