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Inflation Rate Forecasting: Extreme Learning Machine as a Model Combination Method

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Theory and Applications of Time Series Analysis (ITISE 2019)

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Abstract

Inflation rate forecasting is one most discussed topics on time-series analysis due to its importance on macroeconomic policy. The majority of these papers’ findings point out that forecasting combination methods usually outperform individual models. In this sense, we evaluate a novel method to combine forecasts based on Extreme Learning Machine Method [15], which is becoming very popular but, to the best of our knowledge, has not been used to this purpose. We test Inflation Rate forecasting for a set of American countries, for one, two, three, ten, eleven and twelve steps ahead. The models to be combined are automatically estimated by R forecast package, as SARIMA, Exponential Smoothing, ARFIMA, Spline Regression, and Artificial Neural Networks. Another goal of our paper is to test our model against classical combination methods such Granger Bates, Linear Regression, and Average Mean of models as benchmarks, but also test it against basic forms of new models in the literature, like [8, 10, 26]. Therefore, our paper also contributes to the discussion of forecast combination by comparing versions of some methods that have not been tested against each other. Our results indicate that none of these methods have an indisputable superiority against the others, however, the Extreme Learning Method proved to be the most efficient of all, with the smaller Mean Absolute Error and Mean Squared Error for its predictions.

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Author information

Authors and Affiliations

  1. Brazilian Ministry of Economics, Sao Paulo, Brazil

    Jeronymo Marcondes Pinto

  2. Sao Paulo School of Economics, Sao Paulo, Brazil

    Emerson Fernandes Marçal

Authors
  1. Jeronymo Marcondes Pinto
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  2. Emerson Fernandes Marçal
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Corresponding author

Correspondence to Jeronymo Marcondes Pinto .

Editor information

Editors and Affiliations

  1. Faculty of Sciences, University of Granada, Granada, Spain

    Olga Valenzuela

  2. ETSIIT, CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  3. ETSIIT, CITIC-UGR, University of Granada, Granada, Spain

    Luis Javier Herrera

  4. ETSIIT, CITIC-UGR, University of Granada, Granada, Spain

    Héctor Pomares

  5. ETSIIT, CITIC-UGR, University of Granada, Granada, Spain

    Ignacio Rojas

Appendix

Appendix

See Figs. 5, 6, 7, 8, 9, 10, 11, 12.

Fig. 1
figure 1

Mean absolute error and mean squared error for Brazilian forecasting exercise

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Fig. 2
figure 2

Mean absolute error and mean squared error for Chilean forecasting exercise

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Fig. 3
figure 3

Mean absolute error and mean squared error for Mexican forecasting exercise

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Fig. 4
figure 4

Mean absolute error and mean squared error for Peruvian forecasting exercise

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Fig. 5
figure 5

Mean absolute error and mean squared error for USA forecasting exercise

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Fig. 6
figure 6

Mean absolute error and mean squared error for Canada forecasting exercise

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Fig. 7
figure 7

Statistical adjustment of Elm method for Brazil, from one up to twelve steps ahead forecasts

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Fig. 8
figure 8

Statistical adjustment of Elm method for Chile, from one up to twelve steps ahead forecasts

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Fig. 9
figure 9

Statistical adjustment of Elm method for Mexico, from one up to twelve steps ahead forecasts

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Fig. 10
figure 10

Statistical adjustment of Elm method for Peru, from one up to twelve steps ahead forecasts

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Fig. 11
figure 11

Statistical adjustment of Elm method for Canada, from one up to twelve steps ahead forecasts

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Fig. 12
figure 12

Statistical adjustment of Elm method for USA, from one up to twelve steps ahead forecasts

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Pinto, J.M., Marçal, E.F. (2020). Inflation Rate Forecasting: Extreme Learning Machine as a Model Combination Method. In: Valenzuela, O., Rojas, F., Herrera, L.J., Pomares, H., Rojas, I. (eds) Theory and Applications of Time Series Analysis. ITISE 2019. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-56219-9_24

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