Time series forecasting with a non-linear model and the scatter search meta-heuristic

Silva, Carlos Gomes da

doi:10.1016/j.ins.2008.03.024

Cited by 40 publications

(12 citation statements)

References 18 publications

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“…From Eqs. (32), (33), (42) and (43), we see that the analytical formulations of N 0 k ðQ ðn þ pÞÞ and C 0 k ðQ ðn þ pÞÞ can be obtained. On the other hand, the appropriate initial parameters of the forecasting model may be obtained after they are manually tuned by experts, which is useful to improve the convergence speed of the recursive algorithms.…”

Section: Recursive Algorithms Under Parameter Constraints and Expert mentioning

confidence: 87%

New model for system behavior prediction based on belief rule based systems

Zhou

et al. 2010

Information Sciences

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Section: Recursive Algorithms Under Parameter Constraints and Expert mentioning

confidence: 87%

New model for system behavior prediction based on belief rule based systems

Zhou

et al. 2010

Information Sciences

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“…Scatter search [16,55] is an evolutionary population-based metaheuristic that was first introduced in the 1970s as an extensión of the formulation for combining decisión rules and problem constraints [29].…”

Section: Scatter Searchmentioning

confidence: 99%

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

García-Torres

Armañanzas

Bielza

et al. 2013

Information Sciences

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Mass spectrometry (MS) data provide a promising strategy for biomarker discovery. For this purpose, the detection of relevant peakbins in MS data is currently under intense research. Data from mass spectrometry are challenging to analyze because of their high dimensionality and the generally low number of samples available. To tackle this problem, the scientific community is becoming increasingly interested in applying feature subset selection techniques based on specialized machine learning algorithms. In this paper, we present a performance comparison of some metaheuristics: best first (BF), genetic algorithm (GA), scatter search (SS) and variable neighborhood search (VNS). Up to now, all the algorithms, except for GA, have been first applied to detect relevant peakbins in MS data. All these metaheuristic searches are embedded in two different filter and wrapper schemes coupled with Naive Bayes and SVM classifiers.

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“…The seasonal time-series forecasting problem was regarded as a function approximation problem. Silva [35] presented a nonlinear model for time-series forecasting that combines radial basis functions and the ARMA (p,q) structure. A heuristic algorithm is proposed for finding optimal set of parameters.…”

Section: Related Workmentioning

confidence: 99%