Constructive Aggregation and Its Application to Forecasting with Dynamic Ensembles

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Vítor Cerqueira (Author) Other; Fabio Pinto (Author) Other; Luis Torgo (Author) FCUP; Carlos Soares (Author) FEUP; Nuno Moniz (Author) Other

Conference proceedings International

Title: Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part I

Pages: 620-636

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 5 Citations

INSPEC

Other information

Authenticus ID: P-00Q-8KZ

DOI: 10.1007/978-3-030-10925-7_38

Abstract (EN): While the predictive advantage of ensemble methods is nowadays widely accepted, the most appropriate way of estimating the weights of each individual model remains an open research question. Meanwhile, several studies report that combining different ensemble approaches leads to improvements in performance, due to a better trade-off between the diversity and the error of the individual models in the ensemble. We contribute to this research line by proposing an aggregation framework for a set of independently created forecasting models, i.e. heterogeneous ensembles. The general idea is to, instead of directly aggregating these models, first rearrange them into different subsets, creating a new set of combined models which is then aggregated into a final decision. We present this idea as constructive aggregation, and apply it to time series forecasting problems. Results from empirical experiments show that applying constructive aggregation to state of the art dynamic aggregation methods provides a consistent advantage. Constructive aggregation is publicly available in a software package.

Language: English

Type (Professor's evaluation): Scientific

Notes: Data related to this paper are available at: https://github.com/vcerqueira/timeseriesdata. Code related to this paper is available at: https://github.com/vcerqueira/tsensembler. © 2019, Springer Nature Switzerland AG.

No. of pages: 17