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)


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.


Imports forecasting Time series forecasting RNN LSTM ARIMA 


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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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