The Fed paper found that Kalshi's markets provide data that's "valuable to both researchers and policymakers." ...

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 ...

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.

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 ...

A new AI tool to predict the spread of infectious disease outperforms existing state-of-the-art forecasting methods. The tool, created with federal support by researchers at Johns Hopkins and Duke ...

Abstract: The weather prediction results are critical for wind power forecasting, even a minor deviation in wind speed prediction can result in significant power ...

This study addresses the challenges in short-term electrical bus load forecasting. We propose a novel BLformer framework based on an enhanced Patch-TSTransformer. The framework quantifies the ...

Scientists have created a novel probabilistic model for 5-minutes ahead PV power forecasting. The method combines a convolutional neural network with bidirectional long short-term memory, attention ...

In today’s rapidly evolving business landscape, the ability to accurately forecast sales is more critical than ever. Yet, traditional sales forecasting methods are increasingly proving inadequate, as ...