The problem of forecasting time series is very widely debated. In recent years, machine learning algorithms have been very prolific in this area. This paper describes a systematic approach to building a machine learning predictive model for solving optimization problems in the banking sector. A...

The stochastic planning of power production overcomes the drawback of deterministic models by accounting for uncertainties in the parameters. Such planning accounts for demand uncertainties by using scenario sets and probability distributions. However, in previous literature, different scenarios...

The stochastic planning of power production overcomes the drawback of deterministic models by accounting for uncertainties in the parameters. Such planning accounts for demand uncertainties by using scenario sets and probability distributions. However, in previous literature, different scenarios...

Many kernel-based learning algorithms have the computational load scaled with the sample size n due to the column size of a full kernel Gram matrix K. This article considers the Nyström low-rank approximation. It uses a reduced kernel K̂, which is n×m, consisting of m columns (say columns...