Forecasting of Electromagnetic Radiation Time Series: An Empirical Comparative Approach

Authors

  • Zeydin Pala Department of Computer Engineering/Computer Programming University of Mus Alparslan, Mus, 49250, Turkey
  • İbrahim Halil Ünlük Department of Computer Engineering/Computer Programming University of Mus Alparslan, Mus, 49250, Turkey
  • Erkan Yaldız Municipality Information Processing Office, Mus, Turkey

Keywords:

Electromagnetic radiation, ELM, forecasting models, MLP, NNETAR, time series

Abstract

This study compares the performance of time series models for forecasting electromagnetic radiation levels at Yesilce neighborhood in Mus, Turkey. To make successful predictions using EMF time series, which is obtained in the 36-month measurement process using the calibrated Wavecontrol SMP2 device, nine different models were used. In addition to Mean, Naive, Seasonal Naïve, Drift, STLF and TBATS standard models, more advanced ANN models such as NNETAR, MLP and ELM used in the R software environment for forecasting. In order to determine the accuracy of the models used in the EMF time series used in the study, mean absolute error (MAE), relative mean absolute error (RMAE) metrics were used. The best results obtained with NNETAR, Seasonal Naïve, MLP, STLF, TBATS, and ELM models, respectively.

References

C. Chatfield and A. S. Weigend, “Time series prediction: Forecasting the future and understanding the past,” International Journal of Forecasting, vol. 10, p. 161-163, 1996.

G. Redlarski, B. Lewczuk, A. Zak, et al., “The influence of electromagnetic pollution on living organisms: Historical trends and forecasting changes,” BioMed Research International, vol. 2015, pp. 1-18, 2015.

Z. Pala and R. Atici, “Forecasting sunspot time series using deep learning methods,” Solar Physics, vol. 294, no. 50, pp. 1-14, 2019.

K. W. Hipel and A. I. Mcleod, Time Series Modelling of Water Resources and Environmental Systems. Amsterdam, Elsevier 1994.

P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting. 3rd ed., Springer, Switzerland, 2016.

R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice. 2nd ed., Monash University, Australia, 2018.

D. Sena and N. K. Nagwani, “A neural network autoregression model to forecast per capita disposable income,” vol. 11, pp. 13123-13128, 2016.

K. S. R. Krishna, J. L. Narayana, and L. P. Reddy, “ANN models for microstrip line synthesis and analysis,” World Acad. Sci. Eng. Technol., vol. 2, no. 10, pp. 724-8, 2008.

Z. D. Marinković and V. V. Marković, “Temperature-dependent models of low-noise microwave transistors based on neural networks,” Int. J. RF Microw. Comput. Eng., vol. 15, no. 6, pp. 567-77, 2005.

G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1-3, pp. 489-501, 2006.

F. Güneş, P. Mahouti, S. Demirel, M. A. Belen, and A. Uluslu, “Cost-effective GRNN-based modeling of microwave transistors with a reduced number of measurements,” Int. J. Numer. Model. Electron. Networks, Devices Fields, vol. 30, no. 3-4, pp. 1- 12, 2017.

Downloads

Published

2019-08-01

Issue

Section

Articles