Holt-Winters Forecasting for Brazilian Natural Gas Production
DOI:
https://doi.org/10.31686/ijier.vol7.iss6.1559Keywords:
Forecast Models, Holt-Winters, Natural gas, Time SeriesAbstract
Nowadays, the market for natural gas production and its use as a source of energy supply has been growing substantially in Brazil. However, the use of tools that assist the industry in the management of production can be essential for the strategic decision-making process. In this intuit, this work aims to evaluate the formulation of Holt Winter's additive and multiplicative time series to forecast Brazilian natural gas production. A comparison between the models and their forecast play a vital role for policymakers in the strategic plan, and the models estimated production values for the year 2018 based on the information contained in the interval between 2010 and 2017. Therefore, It was verified that the multiplicative method had a good performance so that we can conclude this formulation is ideal for such an application since all the predicted results by this model showed greater accuracy within the 95% confidence interval.
References
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[15] PELLEGRINI, F. R. and FOGLIATTO, F. Comparative study of Winters and Box-Jenkins models for the forecast of seasonal demand. Magazine Produto & Produção, 2000, v. 4, pp. 72-85.
[16] PELLEGRINI, F. R. and FOGLIATTO, F. Steps for implementation of demand forecasting systems - Techniques and case study. Magazine Produto & Produção, 2001, v. 11, p. 43-64.
[17] TULARAM, G. A. and SAEED, T. Oil-Price Forecasting Based on Various Univariate Time-Series Models. American Journal of Operations Research, 2016, v. 6, pp. 226-235. Retrieved from: <http://dx.doi.org/10.4236/ajor.2016.63023>.
[18] WINTERS, P. R. Forecasting sales by exponentially weighted moving averages. Management Science, 1960, v. 6, pp. 324– 342.
[2] ANEEL. National Electric Energy Agency. The government of Brazil, 2008. Retrieved from: <http://www.aneel.gov.br/>. Accessed in: 8 janeiro 2019.
[3] ASSIS, M. V. O.; RODRIGUES, J. J. P. C. and PROENÇA JÚNIOR, M. L. A. A seven dimensional flow analysis to help autonomous network management. InformationSciences. 2014, v. 278, pp. 900-913.
[4] BALLOU, R. H. Supply Chain Management: Enterprise Planning, Organization, and Logistics. Bookman, Porto Alegre, 2001, pp. 616.
[5] BOX, G. E. P. and JENKINS, G. M. Times series analysis, forecasting and control, Holden-Day, 1976.
[6] DONATE, J. P. et al. Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm. Neural Comput & Applic. London, 2013, v. 22, pp. 11–20.
[7] HOLT, C. C. Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting. 2004, v. 20, pp. 5-10.
[8] JERE, S.; KASENSE, B. and CHILYABANYAMA, O. Forecasting Foreign Direct Investment to Zambia: A Time Series Analysis. Open Journal of Statistics. 2017, v. 7, pp. 122-131. Retrieved from: <https://doi.org/10.4236/ojs.2017.71010>.
[9] JUNIOR, H. Q. P. et al. Economia da Energia: Economic Foundations, Historical Evolution and Industrial Organization. Elsevier, Rio de Janeiro, 2007, pp. 416.
[10] MAKRIDAKIS, S.; WHEELWRIGHT, S. and HYNDMAN, R. J. Forecasting Methods and Applications. New York: John Wiley& Sons, 1998.
[11] MINITAB. User' Guide Release 18.1 for Windows, 2017.
[12] MOMIN, B. and CHAVAN, G. Univariate Time Series Models for Forecasting Stationary and Non-stationary Data: A Brief Review, 2017. Retrieved from: <https://www.researchgate.net/publication/315717361_Univariate_Time_Series_Models_for_Forecasting_Stationary_and_Non-stationary_Data_A_Brief_Review>.
[13] MOREIRA, D. A. Production management and operations. Cengage Learning, São Paulo, 2011, pp. 624.
[14] MORETTIN, P. A. and TOLOI, C. M. C. Time series forecast. Edgard Blücher, São Paulo, 2006, pp. 564.
[15] PELLEGRINI, F. R. and FOGLIATTO, F. Comparative study of Winters and Box-Jenkins models for the forecast of seasonal demand. Magazine Produto & Produção, 2000, v. 4, pp. 72-85.
[16] PELLEGRINI, F. R. and FOGLIATTO, F. Steps for implementation of demand forecasting systems - Techniques and case study. Magazine Produto & Produção, 2001, v. 11, p. 43-64.
[17] TULARAM, G. A. and SAEED, T. Oil-Price Forecasting Based on Various Univariate Time-Series Models. American Journal of Operations Research, 2016, v. 6, pp. 226-235. Retrieved from: <http://dx.doi.org/10.4236/ajor.2016.63023>.
[18] WINTERS, P. R. Forecasting sales by exponentially weighted moving averages. Management Science, 1960, v. 6, pp. 324– 342.
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Copyright (c) 2019 Rhuan Carlos Martins Ribeiro, Glauber Tadaiesky Marques, Paulo Cerqueira dos Santos Júnior, José Felipe Souza de Almeida, Pedro Silvestre da Silva Campos, Otavio Andre Chase
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How to Cite
Ribeiro, R. C. M., Marques, G. T., Júnior, P. C. dos S., Almeida, J. F. S. de, Campos, P. S. da S., & Chase, O. (2019). Holt-Winters Forecasting for Brazilian Natural Gas Production. International Journal for Innovation Education and Research, 7(6), 119-129. https://doi.org/10.31686/ijier.vol7.iss6.1559
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