Open AccessForecasting economic time series using chaotic neural networks
Author(s) -
Victor Henrique Gonçalves,
João Luís Garcia Rosa
Publication year - 2018
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/eniac.2018.4470
Subject(s) - artificial neural network , computer science , metric (unit) , chaotic , series (stratigraphy) , artificial intelligence , time series , machine learning , inflation (cosmology) , parametric statistics , parametric model , filter (signal processing) , econometrics , data mining , statistics , mathematics , economics , paleontology , operations management , physics , theoretical physics , computer vision , biology
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.