Abstract: This research focuses on time series data analysis by examining the mutual relationships in the data via autocorrelation and partial autocorrelation, and employs seasonal decomposition to ...

Digital Twin of the Ocean is a continuously updated virtual counterpart of the real ocean that exchanges data in real time ...

AI market forecasting uses predictive analytics and strategic planning tools to anticipate demand shifts, optimize decisions, ...

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...

Researchers in China conceived a new PV forecasting approach that integrates causal convolution, recurrent structures, attention mechanisms, and the Kolmogorov–Arnold Network (KAN). Experimental ...

Until recently, using machine learning for a specific task meant training the system on vast amounts of relevant data. The same was true for data representing a system that changes over time, says SFI ...

Successful test results of a new machine learning (ML) technique developed at Georgia Tech could help communities prepare for extreme weather and coastal flooding. The approach could also be applied ...

ABSTRACT: Time series forecasting is essential for generating predictive insights across various domains, including healthcare, finance, and energy. This study focuses on forecasting patient health ...

Abstract: In the realm of time series forecasting, traditional models often struggle to effectively manage the intricate dependencies and high dimensionality inherent in multivariate data samples.