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Time series forecasting: a test of automated econometric methods

Erick Ferreira and Igor Souza
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Erick Ferreira: UFMG
Igor Souza: UFMG

No 661, Textos para Discussão Cedeplar-UFMG from Cedeplar, Universidade Federal de Minas Gerais

Abstract: The aim of this study is to assess the performance of two well-known algorithms which automate the process of modeling and forecasting time series, each applying a different econometric technic: ARIMA or exponential smoothing. We provide a brief discussion of how these algorithms work and results of a Monte Carlo experiment, which was conducted to evaluate the capabilities of auto.arima and ets, available in Rob Hyndman’s forecast package for the statistical software R, commonly used by economists to study and forecast time series. Over 200.000 synthetic series were simulated, with several different characteristics, used to test both methods and report metrics of correct modeling and out-of-sample forecast errors of the algorithms, on top of which we provide a brief discussion of the successes and shortcomings that happened while applying each algorithm.

Keywords: Time series econometrics; ARIMA; exponential smoothing; auto.arima (search for similar items in EconPapers)
Pages: 13 pages
Date: 2023-10
New Economics Papers: this item is included in nep-for
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