Abstract In this paper, we present and compare several methods for generating scenarios stochastic-programming models by direct selection from historical data. The range standard sampling k -means, through iterative sampling-based methods, to a new moment-based optimization approach. We the on simple portfolio-optimization model show how use them in situation when are selecting whole sequences ...

This article presents a novel nonparametric approach to generate synthetic data using copulas, which are functions that explain the dependency structure of real data. The proposed method addresses several challenges faced by existing generation techniques, such as preservation complex multivariate structures presented in By all information from and verifying generated follows same behavior unde...

Data science has been the foundation of recommender systems for a long time. Over past few decades, various have developed using different data and machine learning methodologies techniques. However, no existing work systematically discusses significant relationships between systems. To bridge this gap, paper aims to investigate from perspective science. Firstly, we introduce types used recomme...

Abstract Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof. These models generate text that is fluent (but often imprecise) and perform quite poorly at selecting appropriate content ordering it coherently. To overcome some of these issues, we propose a neural model with macro planning stage followed by reminiscent trad...