Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 40, Issue 6, Pages 1981-1992Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2012.10.001
Keywords
Time series; Computational intelligence; Neural networks; Support vector machine; Fuzzy rules; Genetic algorithm
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Funding
- European Regional Development Fund in the IT4Innovations Centre of Excellence project [CZ.1.05/1.1.00/02.0070]
- program MSMT-KONTAKT II [LH 12229]
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Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neural networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on seasonal time series from distinct domains on three forecasting horizons. The most important contribution is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series. (C) 2012 Elsevier Ltd. All rights reserved.
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