SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Rodrigo Parracho(rodrigo.parracho@aluno.cefet-rj.br)
2 Fernando Alexandrino(fernando.alexandrino@ifsp.edu.br)
3 Matheus Figueiredo(matheus.figueiredo.1@aluno.cefet-rj.br)
4 Lucas da Silva(lucas.pereira.1@aluno.cefet-rj.br)
5 Bruno de Macedo(bruno.macedo@aluno.cefet-rj.br)
6 Arthur Vaz(arthur.lamblet@aluno.cefet-rj.br)
7 Davi Louback(davi.louback@aluno.cefet-rj.br)
8 Victor Desouzart(victor.desouzart@aluno.cefet-rj.br)
9 Rebecca Salles(rebeccapsalles@acm.org)
10 Fabio Porto (fporto@lncc.br)
11 Diego Carvalho(d.carvalho@ieee.org)
12 Eduardo Ogasawara( eogasawara@ieee.org)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Alexandrino, F., Parracho, R., Carvalho, D., and Ogasawara, E. (2025). Code and Data Repository for LLM on LFD. https://github.com/cefet-rj-dal/tsfm.
2 Ansari, A. F., Stella, L., Turkmen, C., Zhang, X., Mercado, P., Shen, H., Shchur, O., Rangapuram, S. S., Arango, S. P., Kapoor, S., Zschiegner, J., Maddix, D. C., Wang, H., Mahoney, M. W., Torkkola, K., Wilson, A. G., Bohlke-Schneider, M., and Wang, Y. (2024). Chronos: Learning the Language of Time Series. http://arxiv.org/abs/2403.07815.
3 Bahelka, A. and de Weerd, H. (2024). Comparative analysis of Mixed-Data Sampling (MIDAS) model compared to Lag-Llama model for inflation nowcasting. http://arxiv.org/abs/2407.08510.
4 Bao, W., Cao, Y., Yang, Y., Che, H., Huang, J., and Wen, S. (2025). Data-driven stock forecasting models based on neural networks: A review. Information Fusion, 113:102616.
5 Benidis, K., Rangapuram, S. S., Flunkert, V., Wang, Y., Maddix, D., Turkmen, C., Gasthaus, J., Bohlke-Schneider, M., Salinas, D., Stella, L., Aubet, F.-X., Callot, L., and Januschowski, T. (2022). Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. ACM Comput. Surv., 55(6).
6 Box, G. E. P., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control. John Wiley & Sons.
7 FAO (2025). Food and agriculture data. https://www.fao.org/faostat.
8 Goubeaud, M., Jousen, P., Gmyrek, N., Ghorban, F., and Kummert, A. (2021). White Noise Windows: Data Augmentation for Time Series. In 2021 International Conference on Optimization and Applications, ICOA 2021.
9 Gruver, N., Finzi, M., Qiu, S., and Wilson, A. G. (2023). Large Language Models Are Zero-Shot Time Series Forecasters. In Advances in Neural Information Processing Systems, volume 36.
10 Gupta, D., Bhatti, A., and Parmar, S. (2024a). Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models. http://arxiv.org/abs/2409.11302.
11 Gupta, D., Bhatti, A., Parmar, S., Dan, C., Liu, Y., Shen, B., and Lee, S. (2024b). LowRank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting. http://arxiv.org/abs/2405.10216.
12 Han, J., Pei, J., and Tong, H. (2022). Data Mining: Concepts and Techniques. Morgan Kaufmann, Cambridge, MA, 4th edition edition.
13 He, K., Yu, L., and Zou, Y. (2024). Crude oil future price forecasting using pretrained transformer model. Procedia Computer Science, 242:288–293.
14 Hyndman, R. J. and Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
15 Iglesias, G., Talavera, E., González-Prieto, Á., Mozo, A., and Gómez-Canaval, S. (2023). Data Augmentation techniques in time series domain: a survey and taxonomy. Neural Computing and Applications, 35(14):10123 – 10145.
16 Liao, W., Yang, Z., Jia, M., Rehtanz, C., Fang, J., and Porté-Agel, F. (2024). Zero-Shot Load Forecasting with Large Language Models. http://arxiv.org/abs/2411.11350.
17 Lin, N., Yun, D., Xia, W., Palensky, P., and Vergara, P. P. (2024). Comparative Analysis of Zero-Shot Capability of Time-Series Foundation Models in Short-Term Load Prediction. http://arxiv.org/abs/2412.12834.
18 Maior, C. S. and Silva, T. (2024). Time-series failure prediction on small datasets using machine learning. IEEE Latin America Transactions, 22(5):362 – 371.
19 Masini, R. P., Medeiros, M. C., and Mendes, E. F. (2021). Machine learning advances for time series forecasting. Journal of Economic Surveys, 37(1):76 –– 111.
20 McKay, A. and Wolf, C. K. (2023). What can time-series regressions tell us about policy counterfactuals? Econometrica, 91(5):1695–1725.
21 Mello, A., Giusti, L., Tavares, T., Alexandrino, F., Guedes, G., Soares, J., Barbastefano, R., Porto, F., Carvalho, D., and Ogasawara, E. (2024). D-AI2-M: Ethanol Production Forecasting in Brazil Using Data-Centric Artificial Intelligence Methodology. IEEE Latin America Transactions, 22(11):899–910.
22 Ogasawara, E., Castro, A., Borges, H., Carvalho, D., Santos, J., Bezerra, E., and Coutinho, R. (2023). daltoolbox: Leveraging Experiment Lines to Data Analytics. https://cran.rproject.org/web/packages/daltoolbox/index.html.
23 Ogasawara, E., Salles, R., Porto, F., and Pacitti, E. (2025). Event Detection in Time Series. Springer, Switzerland.
24 Pacheco, C., Guimaraes, M., Bezerra, E., Lobosco, D., Soares, J., González, P. H., Andrade, A., De Souza, C. G., and Ogasawara, E. (2022). Exploring Data Preprocessing and Machine Learning Methods for Forecasting Worldwide Fertilizers Consumption. In Proceedings of the International Joint Conference on Neural Networks, volume 2022-July.
25 Petropoulos, F. (2022). Forecasting: theory and practice. International Journal of Forecasting, 38(3):705–871.
26 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., and Rish, I. (2024). Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting. http://arxiv.org/abs/2310.08278.
27 Salles, R., Assis, L., Guedes, G., Bezerra, E., Porto, F., and Ogasawara, E. (2017). A framework for benchmarking machine learning methods using linear models for univariate time series prediction. In Proceedings of the International Joint Conference on Neural Networks, volume 2017-May, pages 2338 – 2345.
28 Salles, R., Belloze, K., Porto, F., Gonzalez, P. H., and Ogasawara, E. (2019). Nonstationary time series transformation methods: An experimental review. Knowledge-Based Systems, 164:274 – 291.
29 Salles, R., Pacitti, E., Bezerra, E., Marques, C., Pacheco, C., Oliveira, C., Porto, F., and Ogasawara, E. (2023). TSPredIT: Integrated Tuning of Data Preprocessing and Time Series Prediction Models. Lecture Notes in Computer Science, 14160 LNCS:41 – 55.
30 Saravanan, H. K., Dwivedi, S., Praveen, P., and Arjunan, P. (2024). Analyzing the Performance of Time Series Foundation Models for Short-term Load Forecasting. In Proceedings of the 2024 11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, BuildSys ’24, pages 346 – 349, New York, NY, USA. Association for Computing Machinery.
31 Semenoglou, A.-A., Spiliotis, E., and Assimakopoulos, V. (2023). Data augmentation for univariate time series forecasting with neural networks. Pattern Recognition, 134.