Forecasting economic time series using chaotic neural networks

Victor H. Gonçalves; João L. G. Rosa

doi:10.5753/eniac.2018.4470

Victor H. Gonçalves USP
João L. G. Rosa USP

DOI: https://doi.org/10.5753/eniac.2018.4470

Resumo

This paper describes the application of KIII, a biologically more plausible neural network model, for forecasting economic time series. K-sets are connectionist models based on neural populations and have been used in many machine learning applications. In this paper, this method was applied to IPCA, a Brazilian consumer price index surveyed by IBGE. The values ranged from August 1994 to June 2017. Experiments were performed using four non-parametric models and seven parametric methods. The statistical metric RMSE was used to compare methods performance. Freeman KIII sets worked well as a filter, but it was not a good prediction method. This paper contributes with the use of non-parametrics models for forecasting inflation in a developing country.

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