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Meta-learning approaches to selecting time series models
2004
Neurocomputing
We present here an original work that applies meta-learning approaches to select models for time-series forecasting. In our work, we investigated two meta-learning approaches, each one used in a di erent case study. Initially, we used a single machine learning algorithm to select among two models to forecast stationary time series (case study I). Following, we used the NOEMON approach, a more recent work in the meta-learning area, to rank three models used to forecast time series of the
doi:10.1016/j.neucom.2004.03.008
fatcat:i4ivdxpe5zam3dvvrlhr2yvgyy