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 Fernando Alexandrino(fernando.alexandrino@ifsp.edu.br)
2 Carla Pacheco(cpacheco@inf.puc-rio.br)
3 Diego Carvalho(d.carvalho@ieee.org)
4 Eduardo Ogasawara( eogasawara@ieee.org)

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

Reference
# Reference
1 Bishop, C. M. (1995). Training with Noise is Equivalent to Tikhonov Regularization. Neural Computation, 7(1):108–116.
2 Box, G. E. P., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control. John Wiley & Sons.
3 Capistrano, B., Chen, L., Ribeiro, M., Pacheco, C., Lobosco, D., Quadros, J., Barreto, M. I., and Ogasawara, E. (2023). Desafios na Predição do Consumo de Pesticidas em Escala Global Usando Aprendizado de Máquina. In Anais do Brazilian e-Science Workshop (BreSci), pages 33–38. SBC.
4 Hastie, T. J. (2017). Generalized Additive Models. Routledge.
5 Haykin, S. O. (2011). Neural Networks and Learning Machines. Pearson Education.
6 Hyndman, R. J. and Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
7 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.
8 Ogasawara, E., Castro, A., Borges, H., Carvalho, D., Santos, J., Bezerra, E., and Coutinho, R. (2023). daltoolbox: Leveraging Experiment Lines to Data Analytics.
9 Ogasawara, E., Martinez, L. C., De Oliveira, D., Zimbrão, G., Pappa, G. L., and Mattoso, M. (2010). Adaptive Normalization: A novel data normalization approach for nonstationary time series. In Proceedings of the International Joint Conference on Neural Networks.
10 Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088):533 – 536.
11 Salles, R., Pacitti, E., Bezerra, E., Porto, F., and Ogasawara, E. (2022). TSPred: A framework for nonstationary time series prediction. Neurocomputing, 467:197 – 202.
12 Um, T. T., Pfister, F. M., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U., and Kulic, D. (2017). Data augmentation of wearable sensor data for Parkinson’s disease monitoring using convolutional neural networks. In ICMI 2017 - Proceedings of the 19th ACM International Conference on Multimodal Interaction, volume 2017-January, pages 216 – 220
13 Wand, M. P. and Jones, M. C. (1994). Kernel Smoothing. CRC Press.
14 Zhang, G. P. (2003). Time series forecasting using a hybrid arima and neural network model. Neurocomputing, 50:159–175.