Deep neural networks estimating event intensities from mixing covariates achieve a prediction-error rate near the nonparametric optimum, and a structured two-step marked ratio estimator outperforms a single network in simulations.
application to high frequency financial data
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Deep learning of point processes for modeling high-frequency data
Deep neural networks estimating event intensities from mixing covariates achieve a prediction-error rate near the nonparametric optimum, and a structured two-step marked ratio estimator outperforms a single network in simulations.