Stochastic models widely used for times series modeling and forecasting are the deseasonalized autoregressive integrated moving average (ARIMA) and autoregressive moving

In this study, new hybrid models are developed by integrating two networks the discrete wavelet transform with an artificial neural network (WANN) model and

models for forecasting time series applied in wind generation based on the combination of time series 828. models with artificial

the sales revenue for the July-September quarter is 80.35% of the average quarter the sales revenue for the October-December quarter is 146.11.% of the average quarter Now to

Based on our discussion above, the electromagnetic space- time changes with the “pure” sapcetime at the same speed ( c − c ' = 0), that means the electromagnetic spacetime is

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The aim of the present study was to utilise data from the SportsLinx project 14 to investigate the distribution of children with inappropriately heavy body weight across the city

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In forecasting mode, models are expected to forecast streamflow from time steps t + 1 to t + L (with L the lead time), knowing both observed meteorological inputs and streamflow

Market conditions and a general return to more relationship-based trading has emphasised the need for more bespoke aggregation services that refl ect a fi rm’s many

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Soetraprawata, "P300 detection using multilayer neural networks based adaptive feature extraction method", International Journal of Brain and Cognitive Sciences,

Suppose a closed convex subset C of a Banach space E has the fixed point property for nonexpansive mappings, and C is either weakly compact, or bounded and separable... Using

autoregressive integrated moving average (ARIMA) time series models for forecasting..

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Of the top 5 questionable medical claim policy types in 2009, General Liability (36 percent) and Commercial Auto (22 percent) had the largest percentage increases and Homeowners

In forecasting mode, models are expected to forecast streamflow from time steps t + 1 to t + L (with L the lead time), knowing both observed meteorological inputs and streamflow

Wind power density forecasting using ensemble predictions and time series models. McSharry (2013), Nonlinear and nonparametric modeling approaches for probabilistic forecasting