Interpretable deep learning network significantly improves tropical cyclone intensity forecast accuracy

Predicting tropical cyclones (TCs) accurately is crucial for disaster mitigation and public safety. Although the forecasting accuracy of TC tracks has improved substantially in recent decades, ...
IEEE

Recurrent Neural Networks for Efficient Solutions to Time-Series Forecasting in Complex Mathematical Models

Abstract: Time series Forecasting plays a major role mainly in areas including finance, health, and meteorology. Although, classical forecast methods, including ARIMA models and feedforward neural ...
IEEE

Probabilistic Approximation of Stochastic Time Series Using Bayesian Recurrent Neural Network

Abstract: In this brief, we investigate the approximation theory (AT) of Bayesian recurrent neural network (BRNN) for stochastic time series forecasting (TSF) from a probabilistic standpoint. Due to ...