Neural networks, as powerful tools for data mining and knowledge engineering, can learn
from data to build feature‐based classifiers and nonlinear predictive models. Training neural …

The key component in forecasting demand and consumption of resources in a supply
network is an accurate prediction of real-valued time series. Indeed, both service …

Wind speed forecasting is a promising solution to improve the efficiency of energy utilization.
In this study, a novel hybrid wind speed forecasting model is proposed. The whole modeling …

In this paper, we address the challenging task of simultaneously optimizing (i) the weights of
a neural network,(ii) the number of neurons for each hidden layer, and (iii) the subset of …

Classification of multivariate time series (MTS) has been tackled with a large variety of
methodologies and applied to a wide range of scenarios. Reservoir computing (RC) …

Research indicates that forecast combination is one of the most important and effective
approaches for time series forecasting. The success of forecast combination depends on …

Anomalies in road surface not only impact road quality but also affect driver safety, mechanic
structure of the vehicles, and fuel consumption. Several approaches have been proposed to …

Echo state network (ESN) is a reservoir computing structure consisting randomly generated
hidden layer which enables a rapid learning and extrapolation process. On the other hand …

In this paper, we elaborate over the well-known interpretability issue in echo-state networks
(ESNs). The idea is to investigate the dynamics of reservoir neurons with time-series …

Recurrent Neural Networks (RNNs) have demonstrated their outstanding ability in sequence
tasks and have achieved state-of-the-art in wide range of applications, such as industrial …