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English Information

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Authors
# Name
1 Cristiane Gea(cristiane.gea@@aluno.cefet-rj.br)
2 Janio Lima(anio.lima@@aluno.cefet-rj.br)
3 Eduardo Bezerra(ebezerra@cefet-rj.br)
4 Eduardo Ogasawara( eogasawara@ieee.org)

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Reference
# Reference
1 Sio-Iong Ao. Applied Time Series Analysis and Innovative Computing. Springer Science & Business Media, April 2010. ISBN 978-90-481-8768-3.
2 Turan G. Bali, Robert F. Engle, and Scott Murray. Empirical Asset Pricing: The Cross Section of Stock Returns. John Wiley & Sons, April 2016. ISBN 978-1-118-09504-1.
3 Les Clewlow and Chris Strickland. Energy Derivatives: Pricing and Risk Management. Lacima Publications, 2000. ISBN 978-0-9538896-0-0.
4 A. Davydenko and R. Fildes. Measuring Forecasting Accuracy: The Case Of Judgmental Adjustments To Sku-Level Demand Forecasts. International Journal of Forecasting, 29(3):510–522, 2013.
5 R.J. Hodrick and E.C. Prescott. Postwar U.S business cycles: An empirical investigation. Journal of Money, Credit and Banking, 29(1):1–16, 1997.
6 R.J. Hyndman and Y. Khandakar. Automatic time series forecasting: The forecast package for r. Journal of Statistical Software, 27(3):1–22, 2008.
7 Rob J. Hyndman and George Athanasopoulos. Forecasting: principles and practice. OTexts, May 2018. ISBN 978-0-9875071-1-2.
8 Wolfgang Härdle. Applied Nonparametric Regression. Cambridge University Press, 1990. ISBN 978-0-521-42950-4.
9 Hemanth P. Kumar and Basavaraj S. Patil. Forecasting volatility trend of INR USD currency pair with deep learning LSTM techniques. In Proceedings 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2018, pages 91–97, 2018.
10 E.H. Lee, C.Wickham, P.A. Beedlow, R.S.Waschmann, and D.T. Tingey. A likelihood-based time series modeling approach for application in dendrochronology to examine the growth-climate relations and forest disturbance history. Dendrochronologia, 45:132–144, 2017.
11 J. Liu, Q. Li, W. Chen, Y. Yan, Y. Qiu, and T. Cao. Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks. International Journal of Hydrogen Energy, pages 5470–5480, 2019.
12 A. Pimenta, C.A.L. Nametala, F.G. Guimarães, and E.G. Carrano. An Automated Investing Method for Stock Market Based on Multiobjective Genetic Programming. Computational Economics, 52(1):125–144, 2018.
13 Robert H. Shumway and David S. Stoffer. Time Series Analysis and Its Applications: With R Examples. Springer, April 2017. ISBN 978-3-319-52452-8.