Ken Gerow, professor emeritus of the University of Wyoming’s Department of Mathematics and Statistics, has co-written a new ...
The predictive analytics market within the banking sector has been witnessing substantial growth in recent years, fueled by the increasing adoption of data-driven decision-making processes and the ...
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 ...
A new ranking methodology places Barry Bonds over Babe Ruth as the game’s best player ever. Statisticians, at least, are cheering. By Alexander Nazaryan Every sport has its arguments over which player ...
Analysis of Multivariate Indoor Building Data: A Comparative Study of Time-Series Clustering Methods
Abstract: Clustering multivariate time-series data is crucial for uncovering complex temporal patterns in dynamic environments, such as building indoor conditions and behavior where variables like ...
Early detection of multidrug-resistant infections has long posed a critical challenge to global health systems. Now, researchers from King Juan Carlos University and the University Hospital of ...
Cerebral physiological signals embody complex neural, vascular, and metabolic processes that provide valuable insight into the brain’s dynamic nature. Profound comprehension and analysis of these ...
Abstract: Traditional statistical time series forecasting models rely on model identification methods to identify the worthiest model variants to investigate; therefore, the model parameters change ...
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