1. Introduction.      

1.1. Introduction

1.2. 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. ARIMA

4.1. Introduction

4.2. Training the models

4.3. The Jarque Bera test

4.4. Generating the training set predictions

4.5. Generating the test set predictions

4.6. Test set errors

4.7. Training set errors

4.8. Overfitting analysis

4.9. Conducting the naive test

4.10. Sensitivity analysis and hyperparameter tuning

4.11. Sensitivity analysis on test set errors

4.12. Forecasts: theory and methodology

4.13. Producing the forecasts

4.14. Final selection of models.

5. ARIMA: Working with Optimal Arima Orders:   

5.1. Download the code

5.2. Introduction

5.3. Stationary series and KPSS test

5.4. Stationarity

5.5. Differencing

5.6. ACF and PACF plots

5.7. The auto ARIMA function

5.8. Fitting the ARIMA models

5.9. Inverting the differencing operation

5.10. Training and test set predictions

5.11. Training and test set errors (MAPE)

5.12. Overfitting and naive model tests

5.13 Generating the Forecasts

5.14 Diagnostic tests

6. Conclusions

6.1. Conclusions