Multi-step ahead forecasting is still an open challenge in time series forecasting. Several
approaches that deal with this complex problem have been proposed in the literature but an …

This paper evaluates the four leading techniques proposed in the literature for construction
of prediction intervals (PIs) for neural network point forecasts. The delta, Bayesian, bootstrap, …

A variety of screening approaches have been proposed to diagnose epileptic seizures,
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. …

The prediction of corporate bankruptcies is an important and widely studied topic since it can
have significant impact on bank lending decisions and profitability. This work presents two …

In this work we present a large scale comparison study for the major machine learning
models for time series forecasting. Specifically, we apply the models on the monthly M3 time …

… Atiya was a recipient of the several awards, such as the Kuwait Prize in 2005. He was an
Associate Editor for the IEEE TRANSACTIONS ON NEURAL NETWORKS from 1998 to 2008. …

… Atiya was supported by NSF’s Engineering Research Center at Caltech. The work of AG …
Atiya, “How delays affect neural dynamics and learning,” IEEE Trans. Neural Networks, vol. …

This paper provides a brief introduction to forecasting in financial markets with emphasis on
commodity futures and foreign exchange. We describe the basic approaches to forecasting, …

We investigate the effects of delays on the dynamics and, in particular, on the oscillatory
properties of simple neural network models. We extend previously known results regarding the …

Estimating the flows of rivers can have significant economic impact, as this can help in agricultural
water management and in protection from water shortages and possible flood damage…