Skip to main content
Log in

Ensembles of neural networks for forecasting of time series of spacecraft telemetry

  • Published:
Optical Memory and Neural Networks Aims and scope Submit manuscript

Abstract

We analyzed different approaches to developing ensembles of neural networks in respect to their forecasting accuracy. We describe a two level model of ensembles of neural networks for forecasting of telemetry time series of spacecraft’s subsystems. A possibility of additional training of these ensembles of neural networks is examined. Our results show that use of ensembles of neural networks with dynamic weighing allows us to reduce the forecasting error.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Hao Quan et al., Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals, IEEE Trans. Neural Networks and Learning Systems, 2013, vol. 25, no. 2, pp. 303–315, ISSN: 2162-237X.

    Google Scholar 

  2. Lysayak, A.S., Prediction of multidimensional time series, Lysayak, A.S. and Ryabko, B.Y., Ed., Vestnik Sib-GUTI,2014, no. 4, pp. 75–88 (in Rusian).

    Google Scholar 

  3. Valipour, M. et al., Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir, J. Hydrol., 2013, vol. 476, pp. 433–441.

    Article  Google Scholar 

  4. Khachumov, V.M., Review of Standards and the concept of monitoring, control and diagnostics of the spacecraft tools building, Software Systems: Theory and Applications, Khachumov, V.M., et al., 2015, vol. 6, no. 3 (26), pp. 21–43 (in Russian).

    MathSciNet  Google Scholar 

  5. Emelyanov, Yu.G., Neural orientation angles and distance of the spacecraft sensor control system, Software Systems: Theory and Applications, Emelyanov, Yu.G., Konstantinov, K.A., Pogodin, S.V., et al., 2010, no. 1 (1), pp. 45–59 (in Russian).

    Google Scholar 

  6. Marushko, Y., Using ensembles of neural networks with different scales of input data for the analysis of telemetry data, Proc. of the XV Intern. PhD Workshop OWD 2013 (Wisla, 2013), Gliwice: Silesian University of Technology, 2013. pp. 386–391.

    Google Scholar 

  7. Kourentzes, Nikolaos et al., Neural network ensemble operators for time series forecasting, Expert Systems Appl., 2014, vol.41, no. 9, pp. 4235–4244, ISSN: 0957-4174.

    Article  Google Scholar 

  8. Elwell, R., Incremental learning of variable rate concept drift, Elwell, Lecture Notes in Computer Science, vol. 5519, Elwell, R. and Polikar, R., Ed., MCS, 2009, pp. 142–151.

    Google Scholar 

  9. Parikh, D., An ensemble-based incremental learning approach to data fusion, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, Parikh, D. and Polikar, R., Ed.,2007, vol. 37, no. 2, pp. 437–450.

    Article  Google Scholar 

  10. Riedmiller, M., A direct adaptive method for faster backpropagation learning: The RPROP algorithm, Riedmiller, M. and Braun, H., Ed., Proceedings of the IEEE International Conference on Neural Networks (ICNN), San Francisco, 1993, pp. 586–591.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to E. E. Marushko.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Marushko, E.E., Doudkin, A.A. Ensembles of neural networks for forecasting of time series of spacecraft telemetry. Opt. Mem. Neural Networks 26, 47–54 (2017). https://doi.org/10.3103/S1060992X17010064

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S1060992X17010064

Keywords

Navigation