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) …
L Wang, Z Wang, H Qu, S Liu - Applied soft computing, 2018 - Elsevier
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 …
C Sun, M Song, S Hong, H Li - arXiv preprint arXiv:2012.02974, 2020 - arxiv.org
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 …