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English Information

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Authors
# Name
1 Antonio Mello(antonio.mello.1@aluno.cefet-rj.br)
2 Eduardo Ogasawara(eogasawara@ieee.org)
3 Diego Carvalho(d.carvalho@ieee.org)

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Reference
# Reference
1 Al-Fattah, S. M. (2020). A new artificial intelligence GANNATS model predicts gasoline demand of Saudi Arabia. Journal of Petroleum Science and Engineering, 194.
2 ANP (2023a). Production of biofuels. Technical report, https://www.gov.br/anp/pt-br/centrais-de-conteudo/dados-abertos/producao-de-biocombustiveis.
3 ANP (2023b). Sales of petroleum derivatives and biofuels. Technical report, https://www.gov.br/anp/pt-br/centrais-de-conteudo/dados-estatisticos/de/vdpb/.
4 Badamchizadeh, S., Latibari, A. J., Tajdini, A., Pourmousa, S., and Lashgari, A. (2021). Modeling Current and Future Role of Agricultural Waste in the Production of Bioethanol for Gasoline Vehicles. BioResources, 16(3):4798 – 4813.
5 Chicco, D., Warrens, M. J., and Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7:1 – 24.
6 da Silva, A. L. and Castañeda-Ayarza, J. A. (2021). Macro-environment analysis of the corn ethanol fuel development in Brazil. Renewable and Sustainable Energy Reviews, 135.
7 Dey, B., Roy, B., Datta, S., and Ustun, T. S. (2023). Forecasting ethanol demand in India to meet future blending targets: A comparison of ARIMA and various regression models. Energy Reports, 9:411 – 418.
8 Dritsaki, C., Niklis, D., and Stamatiou, P. (2021). Oil consumption forecasting using arima models: an empirical study for greece. International Journal of Energy Economics and Policy, 11(4):214–224.
9 Figueira, S. R., Burnquist, H. L., and Bacchi, M. R. P. (2010). Forecasting fuel ethanol consumption in Brazil by time series models: 2006-2012. Applied Economics, 42(7):865 – 874.
10 Fink, R. and Medved, S. (2011). Global perspectives on first generation liquid biofuel production. Turkish Journal of Agriculture and Forestry, 35(5):453 – 459.
11 Hyndman, R. J. and Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
12 Hyndman, R. J. and Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3):1 – 22.
13 Li, Z., Rose, J. M., and Hensher, D. A. (2010). Forecasting automobile petrol demand in Australia: An evaluation of empirical models. Transportation Research Part A: Policy and Practice, 44(1):16 – 38.
14 Nielsen, A. (2019). Practical Time Series Analysis: Prediction with Statistics and Machine Learning. O’Reilly Media, Inc.
15 Ogasawara, E., Castro, A., Borges, H., Carvalho, D., Santos, J., Bezerra, E., and Coutinho, R. (2023). daltoolbox: Leveraging Experiment Lines to Data Analytics.
16 Yu, L., Liang, S., Chen, R., and Lai, K. K. (2022). Predicting monthly biofuel production using a hybrid ensemble forecasting methodology. International Journal of Forecasting, 38(1):3 – 20.