References and data sources

Academic references

1. Vitor Cerqueira; Luis Torgo; Igor Mozetič (2020). Evaluating time series forecasting models: an empirical study on performance estimation methods. Machine Learning, 109, 1997–2028. DOI 10.1007/s10994-020-05910-7.

   Citation key cerqueira2020

2. Christoph Bergmeir; Rob J. Hyndman; Bonsoo Koo (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics & Data Analysis, 120, 70–83. DOI 10.1016/j.csda.2017.11.003.

   Citation key bergmeir2018

3. Shihao Gu; Bryan Kelly; Dacheng Xiu (2020). Empirical Asset Pricing via Machine Learning. The Review of Financial Studies, 33(5), 2223–2273. DOI 10.1093/rfs/hhaa009.

   Citation key gu2020

4. Andrew Ang; Geert Bekaert (2002). International Asset Allocation with Regime Shifts. The Review of Financial Studies, 15(4), 1137–1187. DOI 10.1093/rfs/15.4.1137.

   Citation key ang2002

5. Robert Almgren; Neil Chriss (2001). Optimal execution of portfolio transactions. The Journal of Risk, 3(2), 5–39. DOI 10.21314/JOR.2001.041.

   Citation key almgren2001

6. Abhimanyu Das; Weihao Kong; Rajat Sen; Yichen Zhou (2024). A decoder-only foundation model for time-series forecasting. ICML; Proceedings of Machine Learning Research, 235, 10148–10167.

   Citation key das2024

7. Tilmann Gneiting; Adrian E. Raftery (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association, 102(477), 359–378. DOI 10.1198/016214506000001437.

   Citation key gneiting2007

8. Google Research (Accessed 2026-10-14). google/timesfm-2.5-200m-pytorch model card. Official model documentation.

   Citation key googleresearch2026

9. Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, second edition. Springer.

   Citation key hastie2009

10. Jeffrey Heer; Ben Shneiderman (2012). Interactive Dynamics for Visual Analysis. Communications of the ACM, 55(4), 45–54. DOI 10.1145/2133806.2133821.

    Citation key heer2012

11. Meelis Kull; Miquel Perello Nieto; Markus Kängsepp; Telmo Silva Filho; Hao Song; Peter Flach (2019). Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration. Advances in Neural Information Processing Systems, 32.

    Citation key kull2019

12. Takaya Saito; Marc Rehmsmeier (2015). The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PLOS ONE, 10(3), e0118432. DOI 10.1371/journal.pone.0118432.

    Citation key saito2015

13. Sokolova, Marina; Lapalme, Guy (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management45(4),427–437. DOI10.1016/j.ipm.2009.03.002.

    Citation key sokolova2009

14. Matthew Wang; Yi-Hong Lin; Ilya Mikhelson (2020). Regime-Switching Factor Investing with Hidden Markov Models. Journal of Risk and Financial Management, 13(12), 311. DOI 10.3390/jrfm13120311.

    Citation key wang2020

15. Chuan Guo; Geoff Pleiss; Yu Sun; Kilian Q. Weinberger (2017). On Calibration of Modern Neural Networks. ICML; Proceedings of Machine Learning Research, 70, 1321–1330. Primary record. Status: PRIMARY PEER-REVIEWED SOURCE VERIFIED.

    Citation key guo2017

16. Google Research (Accessed 2026-10-14). google/timesfm-2.5-200m-pytorch model card. Official model documentation. Primary record. Status: OFFICIAL TECHNICAL DOCUMENTATION VERIFIED.

    Citation key googleresearch2026timesfm

17. Scott M. Lundberg; Su-In Lee (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30. Primary record. Status: PRIMARY PEER-REVIEWED SOURCE VERIFIED.

    Citation key lundberg2017

18. Dominik Janzing; Lenon Minorics; Patrick Blöbaum (2020). Feature relevance quantification in explainable AI: A causal problem. AISTATS; Proceedings of Machine Learning Research, 108, 2907–2916. Primary record. Status: PRIMARY PEER-REVIEWED SOURCE VERIFIED.

    Citation key janzing2020

19. Joelle Pineau; Philippe Vincent-Lamarre; Koustuv Sinha; Vincent Lariviere; Alina Beygelzimer; Florence d'Alche-Buc; Emily Fox; Hugo Larochelle (2021). Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program). Journal of Machine Learning Research, 22(164), 1–20. Primary record. Status: PRIMARY PEER-REVIEWED SOURCE VERIFIED.

    Citation key pineau2021

As written in the citation ledgers of the CP2 report draft, read from the repository at build time, with each entry's recorded verification status.

Data sources

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