0.1 Welcome Screen

0.2 - How to use the Actuartech platform

0.3 How to get set up

0.4 - A User Guide for Jupyter Notebooks

0.5 - Other ways to code in Python

1.3 - Background into the Models

1.4 - Further Reading

2.1 - Introduction

2.2 - Relevant Python Packages

2.3 - Preliminary Analysis and Reshaping of Data

2.4 - Conclusion

3.1 - Introduction

3.2 - Lee-Carter Model

3.3 - Cairns-Blake-Dowd Model

3.4 - Automating the Process

3.5 - Conclusion

Week 1 Live Lesson: Chapter 1-3

4.1 - Introduction

4.2 - Deterministic Forecasting: Lee-Carter

4.3 - Deterministic Forecasting: Cairns-Blake-Dowd

4.4 - Stochastic Forecasting: Lee-Carter

4.5 - Stochastic Forecasting: Cairns-Blake-Dowd

4.6 - Conclusion

5.1 - Introduction

5.2 - Data Preparation

5.3 - Theoretical Background

5.4 - Fitting an LSTM using keras and tensorflow

5.5 - Further Advances when Fitting an LSTM using keras and tensorflows

LSTM Notebooks, Lee-Carter & Cairns-Blake-Dowd Packages, and Dataset

Week 2: Forecasting and Introduction to LSTM Neural Networks

6.1 - Conclusion

Python Conventions

New Developments of the Lee-Carter Model for Mortality (PDF)

Modelling and Management of Mortality Risk: A Review (PDF)

A Two-Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration (PDF)

Efficient Use of Data for LSTM Mortality Forecasting (PDF)

Mortality Forecasting using Stacked Regression Ensemble (PDF)

Monte Carlo Valuation of Future Annuity Contracts (PDF)

A Neural Network Extension of the Lee–Carter Model to Multiple Populations (PDF)

Time-Series Forecasting of Mortality Rates using Deep Learning (PDF)

LEE CARTER MODEL (CODE)

Downloadable notebooks and data (zipped)