Accurate prediction of the financial markets can provide many benefits, of which underlying economic stability is probably the most important. This area has understandably attracted a significant amount of interest from the research community, and has inspired a diverse range of approaches with varying degrees of success. Gold is a particular commodity which has attracted considerable attention since it was first smelted for ornaments and jewellery by the Egyptians in 3600BC. In uncertain economic times it is regularly used as a safe-haven commodity, and is why there is considerable attention given to enhancing the accuracy of gold prices prediction methods. Previous attempts at gold price prediction have used a variety of econometric and machine learning techniques. In particular Long Short-Term Networks (LSTMs) and more recently an ensemble of Convolutional Neural Networks (CNNs) and LSTMs have been found to have had considerable level of success in time series prediction. In this research we have conducted a comparative analysis between ARIMA, CNN, LSTM and CNN-LSTM and a recently introduced structure known as Temporal Convolutional Networks (TCNs) on gold price data spanning 20 years. The results show how TCNs produced a RMSE of 15.26 and outperformed both CNN-LSTM and LSTM with RMSE scores of 23.53 and 27.39 respectively.