Affiliations: Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil
Correspondence:
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Corresponding author: Tiago P.F. Lima, Centro de Informática, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, 50740-560, Recife-PE, Brazil. Tel.: +55 81 2126 8430; E-mail: [email protected]
Abstract: We present here a work that applies an automatic construction of ensembles based on the Clustering and Selection (CS) algorithm for time series forecasting. The automatic method, called CSELM, initially finds an optimum number of clusters for training data set and subsequently designates an Extreme Learning Machine (ELM) for each cluster found. For model evaluation, the testing data set are submitted to clustering technique and the nearest cluster to data input will give a supervised response through its associated ELM. Self-organizing maps were used in the clustering phase. Adaptive differential evolution was used to optimize the parameters and performance of the different techniques used in the clustering and prediction phases. The results obtained with the CSELM method are compared with results obtained by other methods in the literature. Five well-known time series were used to validate CSELM.
Keywords: Clustering and selection, ensembles, extreme learning machine, self-organizing maps, time series forecasting, adaptive differential evolution
