Generative moment matching networks (GMMNs) are introduced as dependence models for the joint innovation distribution of multivariate time series (MTS). Following popular copula–GARCH approach modeling dependent MTS data, a framework based on GMMN–GARCH is presented. First, ARMA–GARCH utilized to capture serial within each univariate marginal series. Second, if number large, principal component...

conventionally, regression and time series analyses have been employed in modeling water demand forecasts. in recent years, the relatively new technique of neural networks (nns) has been proposed as an efficient tool for modeling and forecasting. the objective of this study is to investigate the relatively new technique of gmdh – type neural networks for the use of forecasting long – term urban...

based on 11 years of topex/poseidon satellite altimetry and coastal tide gauge sea level observations, four tidal constituents, namely o1, k1, m2 and s2, are modeled for the persian gulf and oman sea using a time-wise approach according to the following details. by selecting the cycle 100 as the reference, 772 points on 12 paths along the track of the altimetry satellite footprints over the per...