The objective of this project is to study forecasting methodolgies for financial applications. The project is intended to compare and contrast classical methodologies from classical statistics and signal processing with machine learning (ML) based methods. The content of this project is organized in a series of Jupyter notebooks for each model type.
| Model | Natural gas dataset | Bond dataset |
|---|---|---|
| Data Overview | Data Overview - Gas.ipynb | Data Overview - Bond.ipynb |
| Linear Models | Linear Models - Gas.ipynb | Linear Models - Bond.ipynb |
| NARX | NARX Models - Gas.ipynb | |
| Recurrent Neural Networks | ||
| Comparison of models and data fusion |
- Dixon, Matthew F., Igor Halperin, and Paul Bilokon. Machine Learning in Finance. Springer International Publishing, 2020.
- Petropoulos, Fotios, Daniele Apiletti, Vassilios Assimakopoulos, Mohamed Zied Babai, Devon K. Barrow, Souhaib Ben Taieb, Christoph Bergmeir et al. "Forecasting: theory and practice." arXiv preprint arXiv:2012.03854 (2020).
- Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice. OTexts, 2018.(https://otexts.com/fpp3/)
- Baltas, Nick, and Dimitrios Karyampas. "Forecasting the equity risk premium: The importance of regime-dependent evaluation." Journal of Financial Markets 38 (2018): 83-102. (https://www.sciencedirect.com/science/article/pii/S1386418116303202)
- De Gooijer, Jan G., and Rob J. Hyndman. "25 years of time series forecasting." International Journal of Forecasting 22, no. 3 (2006): 443-473.
- Schafer, Ronald W. "What Is a Savitzky-Golay Filter?" in IEEE Signal Processing Magazine, vol. 28, no. 4, pp. 111-117, July 2011, doi: 10.1109/MSP.2011.941097.
- Wei, Ching-Zong. "On predictive least squares principles." The Annals of Statistics 20, no. 1 (1992): 1-42.
- Brunton, Steven L., and J. Nathan Kutz. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2019.
- General Interface for Multiple Seasonality Regression Models (TBATS, STLM) by Matt Dancho. (https://business-science.github.io/modeltime/reference/seasonal_reg.html)
- M4 Forecasting Competition: Introducing a New Hybrid ES-RNN Model by Slawek Smyl et. al. (https://eng.uber.com/m4-forecasting-competition/)