Comparative analysis of predictive modeling across key Domains: Insights and applications
| Autor: | Rachid ED-DAOUDI, Altaf ALAOUI, Badia ETTAKI, Jamal ZEROUAOUI |
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| Jazyk: | English<br />French |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Journal of Information Sciences, Vol 22, Iss 2 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1113-4844 2820-6894 |
| DOI: | 10.34874/IMIST.PRSM/jis-v22i2.45112 |
| Popis: | Prediction is widely used for various purposes and in many fields of human activity. The techniques employed for making predictions are a subject of great scientific interest within the research community due to their diversity, level of accuracy, and adaptability to data. The challenge is to determine the factors that affect the choice of an optimal technique suited to each prediction objective. In this article, we conduct a review of models used in the literature to make predictions in different domains to understand the factors influencing the selection of a specific predictive model in relation to their areas of study. A comparative analysis of prediction techniques such as statistical algorithms, Data Mining, and Machine Learning has been performed. It follows that the selection of an adequate prediction technique for the best decision-making should take into account the projection horizon, uncertainty around the prediction, data availability and reliability, and the associated cost of prediction. |
| Databáze: | Directory of Open Access Journals |
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