The AI models are very good at this. Generally, they can achieve the same accuracy as a top-of-the-line, supercomputer-based ...

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 ...

The approach, described in the paper Pattern Sequence Neural Network for Solar Power Forecasting ... can be applied to multiple complementary time series, works by taking a sequence of cluster ...

Scientists have created a novel probabilistic model for 5-minutes ahead PV power forecasting. The method combines a convolutional neural network ... features from time-series inputs and passes ...

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, ...