  Deep learning based forecasting methods have become the methods of choice in
many applications of time series prediction or forecasting often outperforming
other approaches. Consequently, over the last years, these methods are now
ubiquitous in large-scale industrial forecasting applications and have
consistently ranked among the best entries in forecasting competitions (e.g.,
M4 and M5). This practical success has further increased the academic interest
to understand and improve deep forecasting methods. In this article we provide
an introduction and overview of the field: We present important building blocks
for deep forecasting in some depth; using these building blocks, we then survey
the breadth of the recent deep forecasting literature.
