A perturbative approach for enhancing the performance of time series forecasting. (April 2017)

• Record Type: Journal Article
• Authors: de Mattos Neto, Paulo S.G.; Ferreira, Tiago A.E.; Lima, Aranildo R.; Vasconcelos, Germano C.; Cavalcanti, George D.C.
• Is Part Of: Neural networks. Volume 88(2017)
• Page Start: 114
• Page End: 124
• Publication Date: 2017-04
• Subjects: Perturbation theory -- Time series forecasting -- Combination of forecasts -- Artificial neural networks; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2017.02.004
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: This paper proposes a method to perform time series prediction based on perturbation theory. The approach is based on continuously adjusting an initial forecasting model to asymptotically approximate a desired time series model. First, a predictive model generates an initial forecasting for a time series. Second, a residual time series is calculated as the difference between the original time series and the initial forecasting. If that residual series is not white noise, then it can be used to improve the accuracy of the initial model and a new predictive model is adjusted using residual series. The whole process is repeated until convergence or the residual series becomes white noise. The output of the method is then given by summing up the outputs of all trained predictive models in a perturbative sense. To test the method, an experimental investigation was conducted on six real world time series. A comparison was made with six other methods experimented and ten other results found in the literature. Results show that not only the performance of the initial model is significantly improved but also the proposed method outperforms the other results previously published.