1 Introduction
2 Deep Forecasting: A Tutorial
2.1 Notation and Formalization of the Forecasting Problem
2.2 Neural Network Architectures
2.3 Input Transformations
2.4 Output Models and Loss Functions
2.5 Archetypical Architectures
3 Literature Review
3.1 Probabilistic Forecast Models
3.2 Point Forecast Models
3.3 Deep State Space Models
3.4 Multivariate Forecasting
3.5 Physics-based Models
3.6 Global-local
3.7 Intermittent Time Series
3.8 Generalized Adversarial Networks
3.9 Summary and Practical Guidelines
4 Conclusions and Avenues for Future Work