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A Hybrid Singular Spectrum Analysis and Neural Networks for Forecasting Inflow and Outflow Currency of Bank Indonesia

  • SuhartonoEmail author
  • Endah Setyowati
  • Novi Ajeng Salehah
  • Muhammad Hisyam Lee
  • Santi Puteri Rahayu
  • Brodjol Sutijo Suprih Ulama
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 937)

Abstract

This study proposes hybrid methods by combining Singular Spectrum Analysis and Neural Network (SSA-NN) to forecast the currency circulation in the community, i.e. inflow and outflow. The SSA technique is applied to decompose and reconstruct the time series factors which including trend, cyclic, and seasonal into several additive components, i.e. trend, oscillation and noise. This method will be combined with Neural Network as nonlinear forecasting method due to inflow and outflow data have non-linear pattern. This study also focuses on the effect of Eid ul-Fitr as calendar variation factor which allegedly affect inflow and outflow. Thus, the proposed hybrid SSA-NN is evaluated for forecasting time series that consist of trend, seasonal, and calendar variation patterns, by using two schemes of forecasting process, i.e. aggregate and individual forecasting. Two types of data are used in this study, i.e. simulation and real data about the monthly inflow and outflow of 12 currency denominations. The forecast accuracy of the proposed method is compared to ARIMAX model. The results of the simulation study showed that the hybrid SSA-NN with aggregate forecasting yielded more accurate forecast than individual forecasting. Moreover, the results at real data showed that the hybrid SSA-NN yielded as good as ARIMAX model for forecasting of 12 inflow and outflow denominations. It indicated that the hybrid SSA-NN could not successfully handle calendar variation pattern in all series. In general, these results in line with M3 competition conclusion, i.e. more complex methods do not always yield better forecast than the simpler one.

Keywords

Singular spectrum analysis Neural network Hybrid method Inflow Outflow 

Notes

Acknowledgements

This research was supported by DRPM-DIKTI under scheme of “Penelitian Berbasis Kompetensi”, project No. 851/PKS/ITS/2018. The authors thank to the General Director of DIKTI for funding and to anonymous referees for their useful suggestions.

References

  1. 1.
    Sigalingging, H., Setiawan, E., Sihaloho, H.D.: Money Circulation Policy in Indonesia. Bank Indonesia, Jakarta (2004)Google Scholar
  2. 2.
    Apriliadara, M., Suhartono, A., Prastyo, D.D.: VARI-X model for currency inflow and outflow with Eid Fitr effect in Indonesia. In: AIP Conference Proceedings, vol. 1746, p. 020041 (2016)Google Scholar
  3. 3.
    Bowerman, B.L., O’Connell, R.T.: Forecasting and Time Series. Wadsworth Publishing Company, Belmont (1993)zbMATHGoogle Scholar
  4. 4.
    Golyandina, N., Nekrutkin, V., Zhigljavsky, A.A.: Analysis of Time Series Structure: SSA and Related Techniques. Chapman & Hall, Florida (2001)CrossRefGoogle Scholar
  5. 5.
    Broomhead, D.S., King, G.P.: Extracting qualitative dynamics from experimental data. Physica D 20, 217–236 (1986)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Broomhead, D.S., King, G.P.: On the qualitative analysis of experimental dynamical systems. In: Sarkar S (ed.) Nonlinear Phenomena and Chaos, pp. 113–144. Adam Hilger, Bristol (1986)Google Scholar
  7. 7.
    Broomhead, D.S., Jones, R., King, G.P., Pike, E.R.: Singular Spectrum Analysis with Application to Dynamic Systems, pp. 15–27. IOP Publishing, Bristol (1987)Google Scholar
  8. 8.
    Afshar, K., Bigdeli, N.: Data analysis and short-term load forecasting in Iran electricity market using singular spectral analysis (SSA). Energy 36(5), 2620–2627 (2011)CrossRefGoogle Scholar
  9. 9.
    Hassani, H., Zhigljavsky, A.: Singular spectrum analysis: methodology and application to economic data. J. Syst. Sci. Complex. 22, 372–394 (2008)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Zhigljavsky, A., Hassani, H., Heravi, S.: Forecasting European Industrial Production with Multivariate Singular Spectrum Analysis. Springer (2009)Google Scholar
  11. 11.
    Zhang, Q., Wang, B.D., He, B., Peng, Y.: Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting. Springer, China (2011)CrossRefGoogle Scholar
  12. 12.
    Li, H., Cui, L., Guo, S.: A hybrid short term power load forecasting model based on the singular spectrum analysis and autoregressive model. Adv. Electr. Eng. Artic. ID 424781, 1–7 (2014)Google Scholar
  13. 13.
    Lopes, R., Costa, F.F., Lima, A.C.: Singular spectrum analysis and neural network to forecast demand in industry. In: Brazil: The 2nd World Congress on Mechanical, Chemical, and Material Engineering (2016)Google Scholar
  14. 14.
    Sun, M., Li, X., Kim, G.: Precipitation analysis and forecasting using singular spectrum analysis with artificial neural networks. Clust. Comput., 1–8 (2018, in press)Google Scholar
  15. 15.
    Barba, L., Rodriguez, N.: Hybrid models based on singular values and autoregressive methods for multistep ahead forecasting of traffic accidents. Math. Probl. Eng. 2016, 1–14 (2016)CrossRefGoogle Scholar
  16. 16.
    Zhang, X., Wang, J., Zhang, K.: Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by cuckoo search algorithm. Electr. Power Syst. Res. 146, 270–285 (2017)CrossRefGoogle Scholar
  17. 17.
    Lahmiri, S.: Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression. Appl. Math. Comput. 320, 444–451 (2018)MathSciNetGoogle Scholar
  18. 18.
    Khan, M.A.R., Poskitt, D.S.: Forecasting stochastic processes using singular spectrum analysis: aspects of the theory and application. Int. J. Forecast. 33(1), 199–213 (2017)CrossRefGoogle Scholar
  19. 19.
    Lee, M.H., Suhartono, A., Hamzah, N.A.: Calendar variation model based on ARIMAX for forecasting sales data with Ramadhan effect. In: Regional Conference on Statistical Sciences, pp. 349–361 (2010)Google Scholar
  20. 20.
    Zhang, P.G., Patuwo, E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14, 35–62 (1998)CrossRefGoogle Scholar
  21. 21.
    Suhartono: New procedures for model selection in feedforward neural networks. Jurnal Ilmu Dasar 9, 104–113 (2008)Google Scholar
  22. 22.
    Crone, S.F., Kourentzes, N.: Input-variable specification for neural networks - an analysis of forecasting low and high time series frequency. In: International Joint Conference on Neural Networks, pp. 14–19 (2009)Google Scholar
  23. 23.
    Anders, U., Korn, O.: Model selection in neural networks. Neural Netw. 12, 309–323 (1999)CrossRefGoogle Scholar
  24. 24.
    Wei, W.W.S.: Time Series Analysis: Univariate and Multivariate Methods, 2nd edn. Pearson Education, Inc., London (2006)zbMATHGoogle Scholar
  25. 25.
    Zhang, G.P., Qi, M.: Neural network forecasting for seasonal and trend time series. Eur. J. Oper. Res. 160(2), 501–514 (2005)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Makridakis, S., Hibon, M.: The M3-competition: results, conclusions and implications. Int. J. Forecast. 16(4), 451–476 (2000)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Suhartono
    • 1
    Email author
  • Endah Setyowati
    • 1
  • Novi Ajeng Salehah
    • 1
  • Muhammad Hisyam Lee
    • 2
  • Santi Puteri Rahayu
    • 1
  • Brodjol Sutijo Suprih Ulama
    • 1
  1. 1.Department of Statistics, Institut Teknologi Sepuluh NopemberKampus ITS SukoliloSurabayaIndonesia
  2. 2.Department of Mathematical ScienceUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia

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