1 Department of Science, Technology and Engineering, Kibabii University, Bungoma, Kenya. 2 Department of Renewable Energy and Technology, Turkana University College, Lodwar, Kenya. Understanding ...
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 ...
In recent decades, the impact of climate change on natural resources has increased. However, the main challenges associated with the collection of meteorological data include the presence of missing, ...
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 ...
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 ...
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 ...
Recent advances in green chemistry have made multivariate experimental design popular in sample preparation development. This approach helps reduce the number of measurements and data for evaluation ...
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