We explore the added value of deep learning techniques for forecasting and nowcasting in official statistics as an alternative to classic time series models. Several neural network algorithms are ...

Methods A multimodal deep-learning model with transformers was developed for real-time recurrence prediction using baseline clinical, pathological, and molecular data with longitudinal laboratory and ...

Cities are particularly vulnerable to heat stress because paved and densely built-up areas tend to store heat. More frequent and intense heat waves are a growing challenge for public health and urban ...

During the COVID-19 crisis period, when GDP growth became unusually volatile, the advantages of deep learning became even ...

In recent years, scientists have found that machine learning–based weather models can make weather predictions more quickly ...

However, the background of OpenAI's birth is far more complex than a mere technological breakthrough. At that time, the fierce competition between Google and Meta for AI talent led to profound ...

Next, we highlight recent developments in hybrid deep learning models, which combine well-studied statistical models with neural network components to improve pure methods in either category. Lastly, ...