Discover how multivariate models use multiple variables for investment forecasting, risk analysis, and decision-making in ...
Objectives We hypothesise that women with type 2 diabetes and hypertension are less likely than comparable men to receive renin–angiotensin system (RAS)-inhibiting antihypertensive treatment, ...
The AERCA algorithm performs robust root cause analysis in multivariate time series data by leveraging Granger causal discovery methods. This implementation in PyTorch facilitates experimentation on ...
The objective of the present study was to identify the most influent parameters in the composition of groundwater in the municipality of Icapuí, Ceará - Brazil, seeking correlations with the ...
Laboratoire de Matériaux et Environnement (LAME), Université Joseph Ki-Zerbo, Ouagadougou, Burkina Faso. In recent decades, the impact of climate change on natural resources has increased. However, ...
The Multivariate Core Trend inflation rate for March accelerated to 3.0% year-over-year, the worst reading since February 2024, according to the New York Fed today. The re-acceleration was driven ...
Abstract: The past decade has witnessed the success of deep learning-based multivariate time series forecasting in Internet of Things (IoT) systems. However, dynamic variable correlation remains a ...
Monitoring the manufacturing process becomes a challenging task with a huge number of variables in traditional multivariate statistical process control (MSPC) methods. However, the rich information is ...
Abstract: This article explores the use of Fisher discriminant analysis (FDA) as a method for extracting time-resolved information from multivariate environmental time series data. FDA is useful ...
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