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Meta-learning approaches to selecting time series models
- Source :
-
Neurocomputing . Oct2004, Vol. 61 Issue 1-4, p121-137. 17p. - Publication Year :
- 2004
-
Abstract
- 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 different 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 M3-Competition (case study II). The experiments performed in both case studies revealed encouraging results. [Copyright &y& Elsevier]
- Subjects :
- *TIME series analysis
*MACHINE learning
*ARTIFICIAL intelligence
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 61
- Issue :
- 1-4
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 14708575
- Full Text :
- https://doi.org/10.1016/j.neucom.2004.03.008