Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical Methods

Bogdan Oancea, Richard Pospíšil, Nicolae Jula and Cosmin-Ionuț Imbrișcă
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Richard Pospíšil: Department of Economic and Managerial Studies, Palacky University of Olomouc, Olomouc 779 00, Czech Republic
Cosmin-Ionuț Imbrișcă: Department of Applied Economics and Quantitative Analysis, University of Bucharest, Bucharest 050663, Romania

Mathematics, 2021, vol. 9, issue 19, 1-17

Abstract: Even though forecasting methods have advanced in the last few decades, economists still face a simple question: which prediction method gives the most accurate results? Econometric forecasting methods can deal with different types of time series and have good results, but in specific cases, they may fail to provide accurate predictions. Recently, new techniques borrowed from the soft computing area were adopted for economic forecasting. Starting from the importance of economic forecasts, we present an experimental study where we compared the accuracy of some of the most used econometric forecasting methods, namely the simple exponential smoothing, Holt and ARIMA methods, with that of two new methods based on the concept of fuzzy time series. We used a set of time series extracted from the Eurostat database and the R software for all data processing. The results of the experiments show that despite not being fully superior to the econometric techniques, the fuzzy time series forecasting methods could be considered as an alternative for specific time series.

Keywords: economic forecasting; time series; fuzzy sets; R (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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