1. Introduction.

1.1. Introduction

1.2. Multivariate versus univariate models 1.3. Overview of the ten-step methodology 2. Data Preprocessing: 2.1. Introduction

2.2. Data Preprocessing

2.3 Download the original data

3. Dataset split 3.1. Introduction 3.2. Polynomial Features 3.3. Dataset split

4. Dataset scaling

4.1. Introduction 4.2. Scaling the data and the features matrices

5. Deep Neural Networks

5.1. Introduction 5.2. Compiling the models 5,3. Fitting the models 5.4. Drawing the models and clarifying the activation 5.5. Generating the predictions 5.6. Test set set errors 5.7. Training set errors 5.8. Overfitting analysis 5.9. Naive model test 5.10. Sensitivity analysis and hyperparameter tuning 5.11. Sensitivity analysis 5.12. Forecasts: theory and methodology 5.13. Generating the forecasts 5.14. Final selection of the models

6. Conclusion 6.1. Conclusions