Retail demand forecasting is an inherently complex problem as many different time-related factors as well as the correlation of demands in between each and every retail product have to be taken into account. In technical terms, retail demand forecasting is a multivariate timeseries forecasting problem where every single timeseries has to be not only analyzed but also predicted. Hence, added complexity is introduced necessitating the use of advanced methods with machine/deep learning backgrounds. Boosting models, such as XGBoost and LightGBM, are perfect choices with extensive bibliographic background and have been widely used to tackle multivariate timeseries forecasts. Simultaneously, recent advancements in deep neural networks have introduced new promising architectures that are yet to be applied on many different scenarios. Therefore, within this paper, two of those architectures with different core components are introduced, analyzed and applied. The Temporal Convolutional Network based on Convolution and the Temporal Fusion Transformer based on the Transformer architecture, which uses self-attention, are compared to boosting methods as well as standard statistical approaches, namely Exponential Smoothing and Seasonal ARIMA. The results indicate that the deep learning networks are the better choices contributing to the notion that deep learning has extraordinary capabilities in relation to large scale, complex and noisy data and that the aforementioned newly adopted designs are excellent choices for multivariate timeseries forecasting.