Economic & Business Forecasting

The Australian National University

This course covers the theory and practice of economic and business forecasting: from classical time-series methods (ARMA, exponential smoothing) through multivariate models (VAR, state space, dynamic factors) to modern machine learning and neural-network approaches. The materials below are open to everyone.

How to use these materials

Each week has a notebook you can read online, download, or run in the cloud:

  • Read online — click the notebook link; it renders as a webpage with all code and output.
  • Run in Google Colab — click the Colab badge and the notebook opens ready to run in your browser. Nothing to install.
  • Run locally — install Anaconda (includes Python, Jupyter, NumPy, and Matplotlib), then download the .ipynb file from the links below and open it in Jupyter.

Materials

Week Topic Notebook Slides Colab
1 Introduction to Forecasting View · Download PDF Open in Colab
2 Probability & Statistics View · Download PDF Open in Colab
3 ARMA Models View · Download PDF Open in Colab
4 Forecasting with ARMA View · Download PDF Open in Colab
5 Exponential Smoothing & ETS View · Download PDF Open in Colab
6 VAR Models View · Download PDF Open in Colab
7 State Space & Kalman Filter View · Download PDF Open in Colab
8 Dynamic Factor Models View · Download PDF Open in Colab
9 Machine Learning for Forecasting View · Download PDF Open in Colab
10 Neural Networks & Deep Learning View · Download PDF Open in Colab

Notebooks include interactive exercises with hidden solutions — try first, then click to check.

Syllabus

  1. Introduction to forecasting
  2. Probability and statistics for forecasting
  3. ARMA models
  4. Forecasting with ARMA models
  5. Exponential smoothing and ETS
  6. Multiple time series and VAR models
  7. State space models and the Kalman filter
  8. Dynamic factor models
  9. Machine learning for forecasting
  10. Neural networks and deep learning

References

  • Forecasting: Principles and Practice, Rob J. Hyndman and George Athanasopoulos — free online
  • Time Series Analysis, James D. Hamilton (1994)
  • Elements of Statistical Learning, Hastie, Tibshirani, Friedman — main ML reference
  • QuantEcon — open lectures on computational economics

Questions

Found an error in a notebook, or stuck on setup? Email juan.zurita@ed.ac.uk.