Optimizing the extreme learning machine using harmony search for hydrologic time series forecasting

Abstract

Lately, the research related to time series forecasting has been an area of considerable interest in different fields. It is very important to predict the behavior of the time series but it is not an easy task. Several models to aim this issue have been developed over the years, taking into account their peculiarities. Artificial Neural Networks (ANNs) are one of them. ANNs received much attention, and a great number of papers have reported successful experiments and practical tests. In this paper, a hybrid approach is proposed based on Harmony Search (HS) to select the number of hidden neurons and their weights for Extreme Learning Machine (ELM) algorithm, called HS-ELM. In addition, we provide experimental results from the application of our algorithm HS-ELM in real stream flow time series to show its effectiveness and usefulness. © 2012 Springer-Verlag.