Abstract: Optimizing sensor placement is crucial for enhancing the coverage and data-acquisition efficiency of ocean monitoring systems. Traditional approaches primarily rely on univariate ocean data ...
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 ...
Halva—‘grapHical Analysis with Latent VAriables’—is a Python package dedicated to statistical analysis of multivariate ordinal data, designed specifically to handle missing values and latent variables ...
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, ...
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 ...
Leveraging AI to help analyze and visualize data gathered from a variety of data sets enables data-driven insights and fast analysis without the high costs of talent and technology. In today's ...
In recent years, the advent of great technological advances has produced a wealth of very high-dimensional data, and combining high-dimensional information from multiple sources is becoming ...
Multivariate analysis of variance (MANOVA) is a widely used technique for simultaneously comparing means for multiple dependent variables across two or more groups. MANOVA rests on several assumptions ...
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