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.
Highlights
The implementations of the econometric times series forecasting methods used in our experiments, the simple exponential smoothing, Holt, and the Auto Regressive Integrated Moving Average (ARIMA) method, were those provided by the forecast R package [39,40], which has an automatic procedure for setting the optimal parameters of them
One of the biggest challenges for researchers in the field of time series forecasting is the selection of the best method to compute predictions
Classical econometric techniques for time series analysis and forecasting are well understood but new methods coming from the soft computing area have emerged in this field
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Economic forecasts involve the creation of statistical models with the input of selected crucial variables, usually in an effort to focus on the future values for predicted economic indicators. Singh [13,15] proposed a new and computationally simplified method of fuzzy time series forecasting based on difference parameters He tested his method on the same data series regarding students’ enrollment at the University of Alabama and compared the forecasted values obtained with his method against the results obtained by other existing methods to argue in favor of his method. Huarng [16] proposed a new heuristic model for time series forecasting using heuristic increasing and decreasing relations in order to increase the forecast accuracy He tested his method on the same enrollments data and on Taiwan Future Exchange data.
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