Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly
emerging efficient machine learning techniques which offer promising solutions for …

Financial markets play an important role on the economical and social organization of
modern society. In these kinds of markets, information is an invaluable asset. However, with …

In this paper, the accuracy of a hybrid machine learning technique for solar radiation
prediction based on some meteorological data is examined. For this aim, a novel method …

Stock index price prediction is prevalent in both academic and economic fields. The index
price is hard to forecast due to its uncertain noise. With the development of computer …

In this paper, a new hybrid approach by combining the Support Vector Machine (SVM) with
Wavelet Transform (WT) algorithm is developed to predict horizontal global solar radiation …

In this paper, the support vector regression (SVR) methodology was adopted to estimate the
horizontal global solar radiation (HGSR) based upon sunshine hours (n) and maximum …

In this paper, an online sequential extreme learning machine with kernels (OS-ELMK) has
been proposed for nonstationary time series prediction. An online sequential learning …

Accurate estimation of reference evapotranspiration (ET 0) is needed for planning and
managing water resources and agricultural production. The FAO-56 Penman–Monteith …

Probabilistic time series forecasting aims at estimating future probabilistic distributions
based on given time series observations. It is a widespread challenge in various tasks, such …

The dew point temperature is a significant element particularly required in various
hydrological, climatological and agronomical related researches. This study proposes an …