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- W326967972 abstract "This paper describes a new type of ensembles that aims at improving the predictive performance of these approaches in time series forecasting. Ensembles are recognised as one of the most successful approaches to prediction tasks. Previous theoretical studies of ensembles have shown that one of the key reasons for this performance is diversity among ensemble members. Several methods exist to generate diversity. The key idea of the work we are presenting here is to propose a new form of diversity generation that explores some specic properties of time series prediction tasks. Our hypothesis is that the resulting ensemble members will be better at addressing dierent dynamic regimes of time series data. Our large set of experiments conrms that the methods we have explored for generating diversity are able to improve the performance of the equivalent ensembles with standard diversity generation procedures." @default.
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- W326967972 date "2014-01-01" @default.
- W326967972 modified "2023-09-24" @default.
- W326967972 title "Ensembles for Time Series Forecasting" @default.
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