Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical Methods
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/19/2517 |
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| author | Bogdan Oancea Richard Pospíšil Marius Nicolae Jula Cosmin-Ionuț Imbrișcă |
| author_facet | Bogdan Oancea Richard Pospíšil Marius Nicolae Jula Cosmin-Ionuț Imbrișcă |
| author_sort | Bogdan Oancea |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:55:31Z |
| format | Article |
| id | doaj.art-e73c9464fbd2419494b76e7c1624b122 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T06:55:31Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-e73c9464fbd2419494b76e7c1624b1222023-11-22T16:31:31ZengMDPI AGMathematics2227-73902021-10-01919251710.3390/math9192517Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical MethodsBogdan Oancea0Richard Pospíšil1Marius Nicolae Jula2Cosmin-Ionuț Imbrișcă3Department of Applied Economics and Quantitative Analysis, University of Bucharest, Bucharest 050663, RomaniaDepartment of Economic and Managerial Studies, Palacky University of Olomouc, Olomouc 779 00, Czech RepublicDepartment of Applied Economics and Quantitative Analysis, University of Bucharest, Bucharest 050663, RomaniaDepartment of Applied Economics and Quantitative Analysis, University of Bucharest, Bucharest 050663, RomaniaEven 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.https://www.mdpi.com/2227-7390/9/19/2517economic forecastingtime seriesfuzzy setsR |
| spellingShingle | Bogdan Oancea Richard Pospíšil Marius Nicolae Jula Cosmin-Ionuț Imbrișcă Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical Methods Mathematics economic forecasting time series fuzzy sets R |
| title | Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical Methods |
| title_full | Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical Methods |
| title_fullStr | Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical Methods |
| title_full_unstemmed | Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical Methods |
| title_short | Experiments with Fuzzy Methods for Forecasting Time Series as Alternatives to Classical Methods |
| title_sort | experiments with fuzzy methods for forecasting time series as alternatives to classical methods |
| topic | economic forecasting time series fuzzy sets R |
| url | https://www.mdpi.com/2227-7390/9/19/2517 |
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