Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
It serves as an introductory resource for researchers and practitioners interested in understanding and applying deep learning in time series forecasting, but it is incremental as it surveys existing work rather than presenting new findings.
This article provides a tutorial and literature survey on deep learning methods for time series forecasting, highlighting their practical success in outperforming other approaches and achieving top rankings in competitions like M4 and M5.
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.