Chapter Given N forecasting models, what to do?
This work evaluates the forecasting performances of different models using data on Italian unemployment and employment rates over the years 2004-2022 at the monthly frequency. The logic of this work is inspired by the series of M-Competitions, i.e. the tradition of competitions organized to test the...
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| Formato: | Online |
| Idioma: | inglês |
| Publicado em: |
Firenze University Press, Genova University Press
2023
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| Acesso em linha: | ONIX_20230803_9791221501063_123 |
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| _version_ | 1869514895985213440 |
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| author | Culotta, Fabrizio |
| author_browse | Culotta, Fabrizio |
| author_facet | Culotta, Fabrizio |
| author_sort | Culotta, Fabrizio |
| collection | Directory of Open Access Books |
| description | This work evaluates the forecasting performances of different models using data on Italian unemployment and employment rates over the years 2004-2022 at the monthly frequency. The logic of this work is inspired by the series of M-Competitions, i.e. the tradition of competitions organized to test the forecasting performances of classical and innovative models. Given N competing models, only one winner is selected. The types of forecasting models range from the Exponential Smoothing family to ARIMA-like models, to their hybridization, to machine learning and neural network engines. Model combinations through various ensemble techniques are also considered. Once the observational period is split between the training and test set, the estimated forecasting models are ranked in terms of fitting on the training set and in terms of their forecast accuracy on the test set. Results confirm that it does not exist yet a single superior universal model. On the contrary, the ranking of different forecasting models is specific to the adopted training set. Secondly, results confirm that performances of machine learning and neural network models offer satisfactory alternatives and complementarities to the traditional models like ARIMA and Exponential Smoothing. Finally, the results stress the importance of model ensemble techniques as a solution to model uncertainty as well as a tool to improve forecast accuracy. The flexibility provided by a rich set of different forecasting models, and the possibility of combining them, together represent an advantage for decision-makers often constrained to adopt solely pure, not-combined, forecasting models. Overall, this work can represent a first step toward the construction of a semi-automatic forecasting algorithm, which has become an essential tool for both trained and untrained eyes in an era of data-driven decision-making. |
| format | Online |
| id | doab-20.500.12854ir-112105 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Firenze University Press, Genova University Press |
| publisherStr | Firenze University Press, Genova University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1121052025-07-17T10:01:35Z Chapter Given N forecasting models, what to do? Culotta, Fabrizio Forecasting performances M-Competitions Model types Model ensemble techniques Decision-making and forecast accuracy thema EDItEUR::J Society and Social Sciences thema EDItEUR::J Society and Social Sciences This work evaluates the forecasting performances of different models using data on Italian unemployment and employment rates over the years 2004-2022 at the monthly frequency. The logic of this work is inspired by the series of M-Competitions, i.e. the tradition of competitions organized to test the forecasting performances of classical and innovative models. Given N competing models, only one winner is selected. The types of forecasting models range from the Exponential Smoothing family to ARIMA-like models, to their hybridization, to machine learning and neural network engines. Model combinations through various ensemble techniques are also considered. Once the observational period is split between the training and test set, the estimated forecasting models are ranked in terms of fitting on the training set and in terms of their forecast accuracy on the test set. Results confirm that it does not exist yet a single superior universal model. On the contrary, the ranking of different forecasting models is specific to the adopted training set. Secondly, results confirm that performances of machine learning and neural network models offer satisfactory alternatives and complementarities to the traditional models like ARIMA and Exponential Smoothing. Finally, the results stress the importance of model ensemble techniques as a solution to model uncertainty as well as a tool to improve forecast accuracy. The flexibility provided by a rich set of different forecasting models, and the possibility of combining them, together represent an advantage for decision-makers often constrained to adopt solely pure, not-combined, forecasting models. Overall, this work can represent a first step toward the construction of a semi-automatic forecasting algorithm, which has become an essential tool for both trained and untrained eyes in an era of data-driven decision-making. 2023-08-08T05:21:43Z 2023-08-08T05:21:43Z 2023-08-03T15:07:08Z 2023 chapter ONIX_20230803_9791221501063_123 2704-5846 https://library.oapen.org/handle/20.500.12657/74927 9791221501063 https://directory.doabooks.org/handle/20.500.12854/112105 eng Proceedings e report open access image/png image/jpeg Attribution 4.0 International Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/74927/1/9791221501063-55.pdf https://library.oapen.org/bitstream/20.500.12657/74927/1/9791221501063-55.pdf Firenze University Press, Genova University Press 10.36253/979-12-215-0106-3.55 10.36253/979-12-215-0106-3.55 74113d79-2268-4658-88bb-6e8757c543b0 ASA 2022 Data-Driven Decision Making 9791221501063 6 Florence open access |
| spellingShingle | Forecasting performances M-Competitions Model types Model ensemble techniques Decision-making and forecast accuracy thema EDItEUR::J Society and Social Sciences thema EDItEUR::J Society and Social Sciences Culotta, Fabrizio Chapter Given N forecasting models, what to do? |
| title | Chapter Given N forecasting models, what to do? |
| title_full | Chapter Given N forecasting models, what to do? |
| title_fullStr | Chapter Given N forecasting models, what to do? |
| title_full_unstemmed | Chapter Given N forecasting models, what to do? |
| title_short | Chapter Given N forecasting models, what to do? |
| title_sort | chapter given n forecasting models what to do |
| topic | Forecasting performances M-Competitions Model types Model ensemble techniques Decision-making and forecast accuracy thema EDItEUR::J Society and Social Sciences thema EDItEUR::J Society and Social Sciences |
| topic_facet | Forecasting performances M-Competitions Model types Model ensemble techniques Decision-making and forecast accuracy thema EDItEUR::J Society and Social Sciences thema EDItEUR::J Society and Social Sciences |
| url | ONIX_20230803_9791221501063_123 |
| work_keys_str_mv | AT culottafabrizio chaptergivennforecastingmodelswhattodo |