[1] Makridakis et al. 2023. The M6 forecasting competition: Bridging the gap between forecasting and investment decisions arXiv. http://arxiv.org/abs/2310.13357

[2] Makridakis et al. 2023. Statistical, machine learning and deep learning forecasting methods: Comparisons and ways forward, Journal of the Operational Research Society, 74:3, 840-859.

[3] Tim et al. 2022. Forecasting with trees. International Journal of Forecasting 38: 1473-1481

[4] Bryan et al. Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting, International Journal of Forecasting, 37(4):1748-1764, 2021.

[5] Konstantinos et al. 2022. Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. ACM Computing Surveys, Volume 55 Issue 6 Article No.: 121, pp 1?36.

[6] Spiros et al. 2023. "A Glimpse into the Future of Forecasting Software," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 71, pages 50-54, Q4.

[7] Michele et al. 2023. How Will Generative AI Influence Forecasting Software?, Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 71, pages 55-61, Q4.

[8] Lichtenstein et al, L. D. (1982). Calibration of probabilities: The state of the art to 1980. In Judgment under Uncertainty: Heuristics and Biases (pp. 306?334). Cambridge University Press

[9] Assefa, S. (2020). Generating Synthetic Data in Finance: Opportunities, Challenges and Pitfalls (SSRN Scholarly Paper 3634235). https://doi.org/10.2139/ssrn.3634235