In this repo you can find the code that I used to write the blog Forecasting in the Age of Foundation Models, which was published in Towards Data Science on 20 July 2024.
In the blog, I benchmark a foundation model for time series forecasting, Lag-Llama, against an XGBoost forecaster.
The results show that Lag-Llama's performance is in line with, but not significantly better than, more traditional approaches. These results are in line with previous literature, such as Shwartz-Ziv, R., & Armon, A. (2021).
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Rasul, K., Ashok, A., Williams, A. R., Ghonia, H., Bhagwatkar, R., Khorasani, A., Bayazi, M. J. D., Adamopoulos, G., Riachi, R., Hassen, N., Biloš, M., Garg, S., Schneider, A., Chapados, N., Drouin, A., Zantedeschi, V., Nevmyvaka, Y., & Rish, I. (2024). Lag-Llama: Towards foundation models for probabilistic time series forecasting. arXiv. https://arxiv.org/abs/2310.08278
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Shwartz-Ziv, R., & Armon, A. (2021). Tabular data: Deep learning is not all you need. arXiv. https://arxiv.org/abs/2106.03253
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Puertos del Estado. (2024). 3106036 - Punto SIMAR Lon: -5.083 - Lat: 43.5, Oleaje. Last accessed: 25/06/2024
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Amat Rodrigo, J., & Escobar Ortiz, J. (2024). skforecast (Version 0.12.1) [Software]. BSD-3-Clause. https://doi.org/10.5281/zenodo.8382788