Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest. But choosing … built from individual stock return forecasts coming from different machine learning models. The online mixture of experts …

cross-section of stock returns over a 52-year sample to analyze trading performance by implementing the machine learning … machine learning relative to Markowitz, decreasing investor trading costs. The results are robust to various settings and …

potential benefits and limitations of adopting unsupervised machine learning clustering as an alternative method to …

Although Machine Learning (ML) in supply chain management (SCM) has become a popular topic, predictive uses of ML in …

. This study investigates the applicability of various statistical machine learning systems in modeling large data in the … analysis (84.5%), k-NN algorithm (80.5%), and Gaussian naïve Bayes (71.3%). Accordingly, the choice of statistical machine … learning algorithm influences classification accuracy related to electric drive fault diagnosis. In addition, decision trees …

independently forecasting each series in a hierarchy using machine learning. We then combine all forecasts to allow a second …

learning state is divided into two categories: attentive and inattentive. In this paper, machine learning techniques are …

The Internet of Things (IoT), cloud computing, and machine learning opened an opportunity for new smart systems. These …

The massive explosion of literature, theory, and methods on all aspects of decision-analytics, machine learning and …