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English Information

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Authors
# Name
1 Zairo Bastos(zairobastos@gmail.com)
2 Carlos Caminha(caminha@ufc.br)
3 Wellington Franco( wellington@crateus.ufc.br)

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Reference
# Reference
1 Bastos, Z., Freitas, J. D., Franco, J. W., and Caminha, C. (2025). Prompt-driven time series forecasting with large language models. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS, pages 309–316. INSTICC, SciTePress.
2 da Conceição, J. S., dos Santos, J. L., and Cavalcante, R. (2020). Ferramenta para análise de séries temporais. Em Anais da XX Escola Regional de Computação Bahia, Alagoas e Sergipe, páginas 272–281, Porto Alegre, RS, Brasil. SBC.
3 Freitas, J. D., Ponte, C., Bomfim, R., and Caminha, C. (2023). The impact of window size on univariate time series forecasting using machine learning. In Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), pages 65–72. SBC.
4 Garza, A. and Mergenthaler-Canseco, M. (2023). Timegpt-1.
5 Gruver, N., Finzi, M., Qiu, S., and Wilson, A. G. (2023). Large language models are zero-shot time series forecasters. Advances in Neural Information Processing Systems, 36:19622–19635.
6 Gu, Q. (2023). LLM-based code generation method for Golamg compiler testing. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pages 2201–2203.
7 Herzen, J., Lassig, F., Piazzetta, S. G., Neuer, T., Tafti, L., Raille, G., Van Pottelbergh, T., Pasieka, M., Skrodzki, A., Huguenin, N., et al. (2022). Darts: User-friendly modern machine learning for time series. Journal of Machine Learning Research, 23(124):1–6.
8 Hyndman, R. J. and Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
9 Jin, M., Wang, S., Ma, L., Chu, Z., Zhang, J. Y., Shi, X., Chen, P.-Y., Liang, Y., Li, Y. F., Pan, S., et al. (2023). Time-llm: Time series forecasting by reprogramming large language models. arXiv preprint arXiv:2310.01728.
10 Mondal, P., Shit, L., and Goswami, S. (2014). Study of effectiveness of time series modeling (arima) in forecasting stock prices. International Journal of Computer Science, Engineering and Applications, 4(2):13.
11 Wang, W., Chen, Z., Chen, X., Wu, J., Zhu, X., Zeng, G., Luo, P., Lu, T., Zhou, J., Qiao, Y., et al. (2024). Visionllm: Large language model is also an open-ended decoder for vision-centric tasks. Advances in Neural Information Processing Systems, 36.
12 Xue, H. and Salim, F. D. (2023). Promptcast: A new prompt-based learning paradigm for time series forecasting. IEEE Transactions on Knowledge and Data Engineering, 36(11):6851–6864.