Abstract: Aiming at the difficulty of high-speed railway load forecasting, this paper proposes a forecasting model based on QPSO-LSTM. The model combines the long and short-term memory capabilities of ...

Amid the myriad discussions about AI – from the astounding amount of money being spent by vendors and enterprises and the debate about actual ROI those businesses are getting to the technology’s ...

Traditional long-term forecasting models are no longer sufficient as electrification, DER growth, EV adoption, extreme weather events and new large loads introduce unprecedented complexity. The future ...

People are now betting on everything. Prediction markets are amplifying those signals. The timing of the U.S. government shutdown. The likelihood of Taylor Swift canceling a tour date. The exact day ...

A new study by Shanghai Jiao Tong University and SII Generative AI Research Lab (GAIR) shows that training large language models (LLMs) for complex, autonomous tasks does not require massive datasets.

Reactions to Kimmel's suspension, Trump publicly rebukes Putin, and more Length: Long Speed: 1.0x Every three months, participants in the Metaculus forecasting cup try to predict the future for a ...

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 ...

The innovation at the heart of this research lies in combining Long Short-Term Memory (LSTM) networks and Recurrent Neural Networks (RNNs) to tackle financial time series data. These architectures ...