Clustering preprocessing to improve time series forecasting

Article type: Other

Authors: Martínez-Álvarez, Francisco

Affiliations: Department of Computer Science, Pablo de Olavide University of Seville, Seville, Spain. E-mail: [email protected]

Abstract: This work proposes a novel general-purpose forecasting algorithm. It first extracts patterns from time series using the information provided by certain clustering techniques, which are applied as a first step of the approach. Moreover, the occurrence of data with especially unexpected values (outliers) is also addressed in this work. To deal with these outliers, a new hybrid methodology has been proposed, by inserting and adapting an existing approach based on the discovery of frequent episodes in sequences in the general scheme of prediction.

Keywords: Time series, forecasting, clustering, outliers

DOI: 10.3233/AIC-2010-0485

Journal: AI Communications, vol. 24, no. 1, pp. 97-98, 2011