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References

Academic references and citations used throughout this project.

Financial Machine Learning

  1. Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley.

Statistical Methods

  1. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates.

  2. Welch, B. L. (1947). "The Generalization of 'Student's' Problem when Several Different Population Variances are Involved." Biometrika, 34(1/2), 28–35. DOI

  3. Mann, H. B., & Whitney, D. R. (1947). "On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other." The Annals of Mathematical Statistics, 18(1), 50–60. DOI

  4. Kolmogorov, A. N. (1933). "Sulla determinazione empirica di una legge di distribuzione." Giornale dell'Istituto Italiano degli Attuari, 4, 83–91.

  5. Levene, H. (1960). "Robust tests for equality of variances." In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling, I. Olkin et al., Eds. Stanford University Press, pp. 278–292.

Change Point Detection

  1. Page, E. S. (1954). "Continuous Inspection Schemes." Biometrika, 41(1/2), 100–115. DOI

  2. Adams, R. P., & MacKay, D. J. C. (2007). "Bayesian Online Changepoint Detection." arXiv:0710.3742. arXiv

  3. Sharifi, A., Sun, W., & Seco, L. A. (2025). "Detecting Structural Breaks in Dynamic Environments Using Reinforcement Learning and Bayesian Change Point Models." SSRN. SSRN

Deep Learning

  1. Vaswani, A. et al. (2017). "Attention Is All You Need." Advances in Neural Information Processing Systems, 30. arXiv

  2. Hochreiter, S., & Schmidhuber, J. (1997). "Long Short-Term Memory." Neural Computation, 9(8), 1735–1780.

  3. Wang, Y. et al. (2024). "TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables." NeurIPS. NeurIPS

  4. Liu, Y. et al. (2024). "ExoTST: Exogenous-Aware Temporal Sequence Transformer." arXiv:2410.12184. arXiv

  5. Audrino, F. et al. (2024). "Integrating LSTM and Transformer-Based Sentiment for Stock Price Forecasting." Journal of Economic Analysis, 4(3). Link

Machine Learning

  1. Chen, T., & Guestrin, C. (2016). "XGBoost: A Scalable Tree Boosting System." KDD, 785–794. arXiv

  2. Breiman, L. (2001). "Random Forests." Machine Learning, 45(1), 5–32.

  3. Friedman, J. H. (2001). "Greedy Function Approximation: A Gradient Boosting Machine." The Annals of Statistics, 29(5), 1189–1232.

  4. Ke, G. et al. (2017). "LightGBM: A Highly Efficient Gradient Boosting Decision Tree." Advances in Neural Information Processing Systems, 30.

  5. Wolpert, D. H. (1992). "Stacked Generalization." Neural Networks, 5(2), 241–259.

Reinforcement Learning

  1. Watkins, C. J. C. H., & Dayan, P. (1992). "Q-learning." Machine Learning, 8(3-4), 279–292.

  2. Mnih, V. et al. (2015). "Human-level control through deep reinforcement learning." Nature, 518(7540), 529–533.

Signal Processing

  1. Daubechies, I. (1992). Ten Lectures on Wavelets. SIAM.

  2. Song, J. H., Lopez de Prado, M., Simon, H., & Wu, K. (2014). "Exploring Irregular Time Series Through Non-Uniform Fast Fourier Transform." Proceedings of the International Conference for High Performance Computing, IEEE. SSRN