The method of predicting events based on correlation mapping and Fourier decomposition of relevant time series, which is presented in this paper, is intended for the predictive calculation of signs that an event will occur. As input data for calculating the forecast, in addition to the series...

We develop a distribution regression model under endogenous sample selection. This model is a semi-parametric generalization of the Heckman selection model. It accommodates much richer effects of the covariates on outcome distribution and patterns of heterogeneity in the selection process, and...

This work presents a set of neural network (NN) models specifically designed for accurate and efficient fluid dynamics forecasting. In this work, we show how neural networks training can be improved by reducing data complexity through a modal decomposition technique called higher order dynamic...

Purpose: The purpose of this paper is to enhance the forecasting performance of grey model and generate accurate prediction values of Chinese carbon emissions.Design/methodology/approach: A novel grey prediction model with multi-projection is designed. It adapts to the data characteristics,...

Over 2009-2019 the seemingly inexorable rise in health care’s share of GDP markedly slowed, both in the US and elsewhere. To address whether this slowdown represents a reduced steady-state growth rate or just a temporary pause we specify and estimate a decomposition of health care spending...