Cyclone track prediction is a two dimensional time series prediction problem that involves
latitudes and longitudes which define the position of a cyclone. Recurrent neural networks …

This paper presents a two-dimensional time series prediction approach for cyclone track
prediction using cooperative neuro-evolution of Elman recurrent networks in the South …

Time series prediction relies on past data points to make robust predictions. The span of
past data points is important for some applications since prediction will not be possible …

Climate change issues are continuously on the rise and the need to build models and
software systems for management of natural disasters such as cyclones is increasing …

Although machine learning and statistical methods have been extensively used to study
cyclones, the prediction of cyclone trajectories remains a challenging problem. Matrix neural …

Rapid intensification in tropical cyclones occur where there is dramatic change in wind-
intensity over a short period of time. Recurrent neural networks trained using cooperative …

Multi-step-ahead time series prediction has been one of the greatest challenges for machine
learning. Recurrent neural networks (RNN) can efficiently model temporal sequences and …

Frequently, sequences of state transitions are triggered by specific signals. Learning these
triggered sequences with recurrent neural networks implies storing them as different …

In certain types of problems, such as emerging storms or cyclones, robust prediction is
needed even when partial information is available. Dynamic time series prediction refers to …

In this work we present a novel recurrent neural network architecture designed to model
systems characterized by multiple characteristic timescales in their dynamics. The proposed …