Although the practicability of using wavelet neural networks (WNNs) in nonlinear function approximation has been addressed extensively, selecting the optimal number of hidden nodes and their appropriate initial locations remains a great challenge for WNNs' initialization. The cuckoo search...

An Autoencoder (AE) is an independent feature extractor from data samples and a deep network can be obtained by stacking several AEs. This paper presents a novel hybrid stacked Autoencoder-based Deep Kernel-based Random Vector Functional Link Network (DKRVFLN-AE) for forecasting and trend...

Focuses on a study which developed a framework for forecast and decision horizons. Definition of finite and infinite horizon stochastic optimization problems for a given forecast; Description of the general framework; Conditions for the existence of a solution horizon; Development of sufficient...

We consider the basic problem of refi tting a time series over a finite period of time and formulate it as a stochastic dynamic program. By changing the underlying Markov decision process we are able to obtain a model that at optimality considers historical data as well as forecasts of future...