Conditional prediction of time series using spiral recurrent neural network

Frequently, sequences of state transitions are triggered by specific signals. Learning these triggered sequences with recurrent neural networks implies storing them as different attractors of the recurrent hidden layer dynamics. A challenging test and also useful for application is conditional prediction of sequences giving just the trigger signal as an input and letting the recurrent neural network evolve the sequences automatically. This paper addresses this problem with the spiral recurrent neural network (SpiralRNN) architecture.

• Type: Conference
• Year: 2008
• Where: ESANN
• Authors: Huaien Gao, Rudolf Sollacher
• Keywords: ESANN 2008, Neural Networks, Recurrent Hidden Layer, Recurrent Neural Network, Sequences