Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/7522
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dc.contributor.authorVítor Manuel Cerqueiraen
dc.contributor.authorLuís Torgoen
dc.contributor.authorPinto,Fen
dc.contributor.authorCarlos Manuel Soaresen
dc.date.accessioned2018-03-01T10:05:30Z-
dc.date.available2018-03-01T10:05:30Z-
dc.date.issued2017en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/7522-
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-71246-8_29en
dc.description.abstractThis paper proposes an ensemble method for time series forecasting tasks. Combining different forecasting models is a common approach to tackle these problems. State-of-the-art methods track the loss of the available models and adapt their weights accordingly. Metalearning strategies such as stacking are also used in these tasks. We propose a metalearning approach for adaptively combining forecasting models that specializes them across the time series. Our assumption is that different forecasting models have different areas of expertise and a varying relative performance. Moreover, many time series show recurring structures due to factors such as seasonality. Therefore, the ability of a method to deal with changes in relative performance of models as well as recurrent changes in the data distribution can be very useful in dynamic environments. Our approach is based on an ensemble of heterogeneous forecasters, arbitrated by a metalearning model. This strategy is designed to cope with the different dynamics of time series and quickly adapt the ensemble to regime changes. We validate our proposal using time series from several real world domains. Empirical results show the competitiveness of the method in comparison to state-of-the-art approaches for combining forecasters. © 2017, Springer International Publishing AG.en
dc.languageengen
dc.relation5001en
dc.relation6211en
dc.relation4982en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.titleArbitrated Ensemble for Time Series Forecastingen
dc.typeconferenceObjecten
dc.typePublicationen
Appears in Collections:LIAAD - Articles in International Conferences

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