Abstract
The goal of this paper is the application of the virtual leave-one-out methodology to the selection of optimal neural network structure for time series prediction. The experiments are performed on the real dataset of spatiotemporal crime incidence for forecasting in the time coordinate. Due to the idea of local linearization, the estimation of generalization can be obtained in analytical form; hence, the method is computationally efficient.
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Acknowledgements
The work was funded by the grant DOB-BIO7/05/02/2015 of Polish National Office for Research and Development.
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Jankowski, S., Szymański, Z., Wawrzyniak, Z., Cichosz, P., Szczechla, E., Pytlak, R. (2019). Selection of Neural Network for Crime Time Series Prediction by Virtual Leave-One-Out Tests. In: Valenzuela, O., Rojas, F., Pomares, H., Rojas, I. (eds) Theory and Applications of Time Series Analysis. ITISE 2018. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-26036-1_9
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DOI: https://doi.org/10.1007/978-3-030-26036-1_9
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