AI weather models can accurately predict everyday weather but often fail to forecast rare, unprecedented events like Category ...

Neural networks aim to solve problems that would be impossible or difficult to solve with statistical or classical methods. Two of the most popular time series forecasting neural networks are ...

We present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture. The proposed network contains stacks of dilated convolutions ...

This study is an exploration of where we can expect added value for forecasting and nowcasting time series in official statistics by using deep learning techniques, as an alternative to classic time ...

Huang Gang from the Institute of Atmospheric ... time, using entity embedding techniques to calibrate the model. Additionally, the research team localized the ChebNet graph neural network for ...

Therefore, accurate forecasting ... Neural Network (ANN) as a non-linear black box interpolator tool is used for modeling suspended sediment load which discharges to the Talkherood river mouth, ...