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Harnessing the wisdom of the crowd: ensemble methods for time series forecasting of call center arrivals
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Resumo(s)
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.
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Palavras-chave
Time series forecasting Ensemble methods Bagging Moving block boot-strap Dynamic integration Diversity among base-learners Small dataset