45 papers with code • 1 benchmarks • 11 datasets
Weather Forecasting is the prediction of future weather conditions such as precipitation, temperature, pressure and wind.
The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time.
NGBoost generalizes gradient boosting to probabilistic regression by treating the parameters of the conditional distribution as targets for a multiparameter boosting algorithm.
We cast the weather forecasting problem as an end-to-end deep learning problem and solve it by proposing a novel negative log-likelihood error (NLE) loss function.
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks
Recurrent graph convolutional neural networks are highly effective machine learning techniques for spatiotemporal signal processing.
In addition, using this method, an expectation maximization algorithm can be used to estimate the parameters of the model.
Deep learning applied to weather forecasting has started gaining popularity because of the progress achieved by data-driven models.
In this paper, we forecast high-resolution numeric weather data using both input weather data and observations by providing a novel deep learning architecture.