Predicting periodic and chaotic signals using Wavenets

This thesis discusses forecasting periodic time series using Wavenets with an application in financial time series. Conventional neural networks used for forecasting such as the LSTM and the full convolutional network (FCN) are computationally expensive. The Wavenet uses dilated convolutions which significantly reduces the computational cost...