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Prediction of Imports of Household Appliances in Ecuador Using LSTM Networks

  • Andrés TelloEmail author
  • Ismael Izquierdo
  • Gustavo Pacheco
  • Paúl Vanegas
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1099)

Abstract

Time series forecasting is an important topic widely addressed with traditional statistical models such as regression, and moving average. This work uses the state-of-the-art Long Short-Term Memory (LSTM) Networks to predict Ecuadorian imports of Home Appliances, and to compare the results against those obtained by traditional methods. First, an ARIMA model was used to forecast imports data. Then, the predictions were calculated by a Univariate LSTM network. The time series used in both experiments was the monthly average of imports from 1996 to April 2019. In addition, time series of GDP Growth, Population, and Inflation were included in the model to test prediction improvements. The performance of the models was assessed comparing the Mean Squared, Root Mean Square and Mean Absolute Error metrics. The results show that a LSTM network produces a better fit of the imports data and improved predictions compared against those produced by the ARIMA model. Furthermore, the use of multivariate time series (i.e., GDP Growth, Population, Inflation) data, for the LSTM model, did not produce significant improvements compared to the univariate imports time series.

Keywords

Imports forecasting Time series forecasting RNN LSTM ARIMA 

References

  1. 1.
    Alley, R.B., Emanuel, K.A., Zhang, F.: Advances in weather prediction. Science 363(6425), 342–344 (2019)CrossRefGoogle Scholar
  2. 2.
    Araújo, M.G., Magrini, A., Mahler, C.F., Bilitewski, B.: A model for estimation of potential generation of waste electrical and electronic equipment in Brazil. Waste Manag. 32(2), 335–342 (2012)CrossRefGoogle Scholar
  3. 3.
    Balkin, S.D., Ord, J.K.: Automatic neural network modeling for univariate time series. Int. J. Forecast. 16(4), 509–515 (2000)CrossRefGoogle Scholar
  4. 4.
    Bandara, K., Shi, P., Bergmeir, C., Hewamalage, H., Tran, Q., Seaman, B.: Sales demand forecast in e-commerce using a long short-term memory neural network methodology. arXiv preprint arXiv:1901.04028 (2019)
  5. 5.
    Bianchi, F.M., Maiorino, E., Kampffmeyer, M.C., Rizzi, A., Jenssen, R.: An overview and comparative analysis of recurrent neural networks for short term load forecasting. arXiv preprint arXiv:1705.04378 (2017)
  6. 6.
    Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2015)zbMATHGoogle Scholar
  7. 7.
    Chakraborty, K., Mehrotra, K., Mohan, C.K., Ranka, S.: Forecasting the behavior of multivariate time series using neural networks. Neural Netw. 5(6), 961–970 (1992)CrossRefGoogle Scholar
  8. 8.
    Choi, J.Y., Lee, B.: Combining LSTM network ensemble via adaptive weighting for improved time series forecasting. Math. Probl. Eng. 2018, 8 pages (2018).  https://doi.org/10.1155/2018/2470171. Article ID 2470171 CrossRefGoogle Scholar
  9. 9.
    Greff, K., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2016)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  11. 11.
    Ikhlayel, M.: Differences of methods to estimate generation of waste electrical and electronic equipment for developing countries: Jordan as a case study. Resour. Conserv. Recycl. 108, 134–139 (2016)CrossRefGoogle Scholar
  12. 12.
    Khashei, M., Bijari, M.: A novel hybridization of artificial neural networks and arima models for time series forecasting. Appl. Soft Comput. 11(2), 2664–2675 (2011)CrossRefGoogle Scholar
  13. 13.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  14. 14.
    Kuhn, M., Johnson, K.: Applied Predictive Modeling, vol. 26. Springer, New York (2013)CrossRefGoogle Scholar
  15. 15.
    Kwiatkowski, D., Phillips, P.C., Schmidt, P., Shin, Y.: Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? J. Econometrics 54(1–3), 159–178 (1992)CrossRefGoogle Scholar
  16. 16.
    Manuca, R., Savit, R.: Stationarity and nonstationarity in time series analysis. Physica D: Nonlinear Phenom. 99(2–3), 134–161 (1996)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: International Conference on Machine Learning, pp. 1310–1318 (2013)Google Scholar
  18. 18.
    Samsudin, R., Shabri, A., Saad, P.: A comparison of time series forecasting using support vector machine and artificial neural network model. J. Appl. Sci. 10(11), 950–958 (2010)CrossRefGoogle Scholar
  19. 19.
    Sun, J., Suo, Y., Park, S., Xu, T., Liu, Y., Wang, W.: Analysis of bilateral trade flow and machine learning algorithms for GDP forecasting. Eng. Technol. Appl. Sci. Res. 8(5), 3432–3438 (2018)Google Scholar
  20. 20.
    Torres, J.F., Fernández, A., Troncoso, A., Martínez-Álvarez, F.: Deep learning-based approach for time series forecasting with application to electricity load. In: International Work-Conference on the Interplay Between Natural and Artificial Computation, pp. 203–212. Springer, Cham (2017)CrossRefGoogle Scholar
  21. 21.
    Wang, F., Huisman, J., Stevels, A., Baldé, C.P.: Enhancing e-waste estimates: improving data quality by multivariate input-output analysis. Waste Manag. 33(11), 2397–2407 (2013)CrossRefGoogle Scholar
  22. 22.
    Wu, Y., Yuan, M., Dong, S., Lin, L., Liu, Y.: Remaining useful life estimation of engineered systems using vanilla lstm neural networks. Neurocomputing 275, 167–179 (2018)CrossRefGoogle Scholar
  23. 23.
    Yi, J., Prybutok, V.R.: A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. Environ. Pollut. 92(3), 349–357 (1996)CrossRefGoogle Scholar
  24. 24.
    Zhang, G.P.: Time series forecasting using a hybrid arima and neural network model. Neurocomputing 50, 159–175 (2003)CrossRefGoogle Scholar
  25. 25.
    Zhang, H., Wang, X., Cao, J., Tang, M., Guo, Y.: A multivariate short-term traffic flow forecasting method based on wavelet analysis and seasonal time series. Appl. Intell. 48, 3827–3838 (2018)CrossRefGoogle Scholar
  26. 26.
    Zou, H., Xia, G., Yang, F., Wang, H.: An investigation and comparison of artificial neural network and time series models for chinese food grain price forecasting. Neurocomputing 70(16–18), 2913–2923 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Andrés Tello
    • 1
    Email author
  • Ismael Izquierdo
    • 1
  • Gustavo Pacheco
    • 1
  • Paúl Vanegas
    • 1
    • 2
  1. 1.Department of Space and PopulationUniversity of CuencaCuencaEcuador
  2. 2.Faculty of ChemistryUniversity of CuencaCuencaEcuador

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