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 ...

Designing robust loss functions is essential for improving deep learning performance on noisy and sparse time series. We propose the Minkowski–Log–Cosh (MLC) loss, a hybrid formulation that integrates ...

If you're like me and you went to bed before the end of the 2025 World Series Game 3 that lasted 18 INNINGS (!!!!), I'm here for you. So what time did it all end? That would be around 2:51 a.m. ET. So ...

Shares of red-hot GE Aerospace have taken a little breather following strong Q3 results and increased guidance. The stock had risen 75% this year prior to reporting Tuesday morning, but has pulled ...

Researchers have modeled a hybrid financing scheme combining contracted and merchant components to improve the bankability of PV-battery energy storage system (PV-BESS) assets, using a Bayesian LSTM ...

The Dodgers spent $400 million very well, and they’ll have a solid rotation advantage, enough to counteract a well-rounded, together Toronto team. The Dodgers are built for this, their star power and ...

Short-term forecasts are used for scheduling staff and customer service. Medium-term forecasts are used for purchasing supplies and materials. Long-term forecasts are used for strategic ...

Abstract: The rise of decentralized energy sources and renewables demands advanced grid planning, with short-term load forecasting (STLF) playing a crucial role. Energy demand in smart grids is highly ...

In forecasting economic time series, statistical models often need to be complemented with a process to impose various constraints in a smooth manner. Systematically imposing constraints and retaining ...

Background: Accurate forecasting of lung cancer incidence is crucial for early prevention, effective medical resource allocation, and evidence-based policymaking. Objective: This study proposes a ...