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 compared to the FCN with the same number of inputs. Forecasts made on the sine wave shows that the network can successfully fully forecast a sine wave. Forecasts made on the Mackey Glass time series shows that the network can outperform the LSTM and other methods Furthermore, forecasts made on the Lorenz system shows that the network is able to outperform the LSTM. By conditioning the network on the other relevant coordinate, the prediction becomes more accurate and is able to make full forecasts. In a financial application, the network shows less predictive accuracy compared to multivariate dynamic kernel support vector machines.