Pattern Recognition for Time Series Forecasting: A Case Study of a Helicopter’s Flight Dynamics

  • Pedro Fernandes
  • Alexandre C. B. RamosEmail author
  • Danilo Pereira Roque
  • Marcelo Santiago de Sousa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 738)


This paper presents a method for time series forecasting based on pattern recognition. As the system receives samples of time series, each of them representing one variable from the set of variables that describe the behavior of an application model, these samples are evaluated using a PCA algorithm, where each sample is represented by a feature vector. Different feature vectors (each of them representing a different sample of a particular case) are compared for pattern recognition. Once this sequence of steps is well performed, it’s possible to estimate time series for different states between those represented by the previously analyzed samples. As an example for application of this method, a case study is presented for some variables under specific flight conditions. The chosen application for this case study, helicopter flight dynamics is a relevant study, for it can be used, for example, to provide precise data to a flight simulator, which implies in an important issue for pilot training, and subsequently, this type of application may help reducing the probability of pilot's faults in real flight missions. To demonstrate the applicability of the method, this paper shows results obtained when the system generated forecasts for flight dynamics variables in a specific scenario of initial conditions and while the helicopter performed a maneuver of response to collective command. Finally, some considerations are made about the work shown in this paper as the results, discussions and conclusions are presented.


Helicopter flight dynamics Pattern recognition Flight simulator 



We thank UNIFEI for research support and FAPEMIG for financial support.



  1. 1.
    X. You, M. Ji, H. Han, The effects of risk perception and flight experience on airline pilots’ locus of control with regard to safety operation behaviors. Accid. Anal. Prev. 9, 131–139 (2013) CrossRefGoogle Scholar
  2. 2.
    J.S. Melo, M.S.R. Tadeucci, A atividade aérea e uso de simulador de voo, XIV Encontro Latino Americano, 2010Google Scholar
  3. 3.
    J. Ryder, T. Santarelli, J. Scolaro, J. Hicinbothom, W. Zachary, Comparison of cognitive model uses in intelligent training systems, in Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol 4, 2000, pp. 374–377Google Scholar
  4. 4.
    S.S. Cunha Jr., M.S. de Sousa, D.P. Roque, A.C.B. Ramos, P. Fernandes Jr., Dynamic simulation of the flight behavior of a rotary-wing aircraft. Inf. Technol. N. Gener. 13, 1087–1099 (2016)Google Scholar
  5. 5.
    P.V.M Simplício, Helicopter nonlinear flight control using incremental nonlinear dynamic inversion, Delft University of Technology, 2011Google Scholar
  6. 6.
    G.D. Padfield, Helicopter Flight Dynamics, 2nd edn. (Wiley, New York, 2008)Google Scholar
  7. 7.
    I. Lima, C.A.M. Pinheiro, F.A.O. Santos, Inteligência Artificial, 1st edn. (Elsevier, 2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Pedro Fernandes
    • 1
  • Alexandre C. B. Ramos
    • 1
    Email author
  • Danilo Pereira Roque
    • 2
  • Marcelo Santiago de Sousa
    • 2
  1. 1.Federal University of Itajubá, Mathematics and Computing InstituteItajubáBrazil
  2. 2.Federal University of Itajubá, Mechanical Engineering InstituteItajubáBrazil

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