The ability to understand and explain the outcomes of data analysis methods, with regard to
aiding decision-making, has become a critical requirement for many applications. For …

Evaluation of intervention in a multiagent system, for example, when humans should
intervene in autonomous driving systems and when a player should pass to teammates for a …

Under complex conditions, the collaborative control capability of UAV swarms is considered
to be the key to ensuring the stability and safety of swarm flights. However, in complex …

Forecasting models that are trained across sets of many time series, known as global
forecasting models, have recently shown promising results in prestigious forecasting …

Probabilistic time series forecasting is crucial in various fields, including reducing stockout
risks in retail, balancing road network loads, and optimizing power distribution systems …

In this article, we present a modification to the state-of-the-art N-BEATS deep learning
architecture for the univariate time series point forecasting problem for generating …

Cada vez mais o aprendizado de máquina tem sido aplicado para desempenhar atividades
que requeriam a execução por seres humanos, inclusive no âmbito governamental. Nesse …

We introduce a novel forecasting method employing global deep learning models for
estimating the causal effects of interventions across multiple units, incorporating …

This thesis addresses the challenges of causal effect estimation in complex real-world
scenarios by proposing a global forecasting model (GFM) with deep neural networks …

The electricity industry is heavily implementing intelligent control systems to improve
reliability, availability, security, and efficiency. This implementation needs technological …