In the pursuit of accurate and scalable quantitative methods for financial market analysis, focus has shifted from individual stock models to those capturing interrelations between companies their stocks. However, current relational are limited by reliance on predefined relationships exclusive consideration immediate effects. To address these limitations, we present a groundbreaking framework a...

The joint modeling of mean and dispersion (JMMD) provides an efficient method to obtain useful models for the dispersion, especially in problems robust design experiments. However, literature on JMMD there are few works dedicated variable selection this theme is still a challenge. In article, we propose procedure selecting variables JMMD, based hypothesis testing quality model’s fit. A criterio...

Previous work has shown that joint modeling of two Natural Language Processing (NLP) tasks are effective for achieving better performances for both tasks. Lots of task-specific joint models are proposed. This paper proposes a Hierarchical Long Short-Term Memory (HLSTM) model and some its variants for modeling two tasks jointly. The models are flexible for modeling different types of combination...

We propose a new model for correlated outputs of mixed type, such as continuous and binary outputs, with a particular focus on joint regression and classification, motivated by an application in constrained optimization for computer simulation modeling. Our framework is based upon multivariate stochastic processes, extending Gaussian process methodology for modeling of continuous multivariate s...