Year Master of Information and Data Science (MIDS) alums Marlon Fu, Austin Ho, Nora Povejsil, Suhas Prasad, and Derek Yao are ...

Inventors at SAS often find novelty in mundane tasks, leading to innovative solutions like optimization algorithms and unique ...

Time series forecasting is the bread and butter of what ... external variables — critical features that existing popular methods lack. These fast and tiny general pre-trained AI models can ...

Applying patient flow forecasting methodology to data sources Patient ... used in patient flow forecasting Statistical models and time series methodologies are typically associated with sales ...

This study introduces a novel short-term load forecasting approach based on Belief Functions Theory (BFT). The proposed method employs information fusion techniques to combine multiple predictors, ...

To this end, advanced time series forecasting methods based on Deep Learning (DL) pose a promising avenue in tackling these challenges. In this study, we present a taxonomy of DL forecasting ...

Classical Decomposition Seasonal and Trend Decomposition Using Loess (STL) Decomposition Seasonal Extraction in ARIMA Time Series (SEATS) Decomposition (developed by the Bank of Spain) X-11, ...

To bridge the modality gap between textual and temporal data, we introduce three meticulously designed cross-modal fine-tuning techniques (see Figure 2): ...

Reservoir computing (RC) is a powerful machine learning module designed to handle tasks involving time ... Taylor's series expansion which simplifies complex functions into smaller and more manageable ...

Rapid Intensification (RI) of tropical cyclones (TCs), defined as an intensity increase of at least 13 m/s within 24 hours, ...