Publicação
Harnessing the wisdom of the crowd: ensemble methods for time series forecasting of call center arrivals
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | pt_PT |
| dc.contributor.advisor | Lavado, Susana | |
| dc.contributor.advisor | Pereira, Gustavo | |
| dc.contributor.author | Kunnemann, Hendrik | |
| dc.date.accessioned | 2025-08-04T09:59:48Z | |
| dc.date.available | 2025-08-04T09:59:48Z | |
| dc.date.issued | 2025-01-29 | |
| dc.date.submitted | 2025-01-29 | |
| dc.description.abstract | This study extends a time series forecasting project (PBL) on a small dataset by exam ining ensemble learning, including homogeneous (bagging) and heterogeneous (Dynamic Integration) approaches. While bagging slightly reduces accuracy (MAPE), it improves stability. By incorporating a novel error-based dynamic pairwise correlation strategy to enhance diversity between base-learners, the Dynamic Weighting with Selection method within Dynamic Integration significantly outperforms the baseline, reducing the error met ric MAPE by nearly 10% and the stability metric by over 20%. These findings highlight the effectiveness of ensemble learning, particularly DWS, for accurate and reliable forecasting in small datasets. | pt_PT |
| dc.identifier.tid | 203962931 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/185994 | |
| dc.language.iso | eng | pt_PT |
| dc.relation | UID/ECO/00124/2013 | pt_PT |
| dc.subject | Time series forecasting | pt_PT |
| dc.subject | Ensemble methods | pt_PT |
| dc.subject | Bagging | pt_PT |
| dc.subject | Moving block boot-strap | pt_PT |
| dc.subject | Dynamic integration | pt_PT |
| dc.subject | Diversity among base-learners | pt_PT |
| dc.subject | Small dataset | pt_PT |
| dc.title | Harnessing the wisdom of the crowd: ensemble methods for time series forecasting of call center arrivals | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Master’s degree in Business Analytics from the Nova School of Business and Economics | pt_PT |