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Time Series Prediction by Perturbed Fuzzy Model.

Authors: Salgado, Paulo; Gouveia, Fernando; Igrejas, Getúlio;

Time Series Prediction by Perturbed Fuzzy Model.

Abstract

This paper presents a fuzzy system approach to the prediction of nonlinear time series and dynamical systems based on a fuzzy model that includes its derivative information. The underlying mechanism governing the time series, expressed as a set of IF–THEN rules, is discovered by a modified structure of fuzzy system in order to capture the temporal series and its temporal derivative information. The task of predicting the future is carried out by a fuzzy predictor on the basis of the extracted rules and by the Taylor ODE solver method. We have applied the approach to the benchmark Mackey-Glass chaotic time series.

Country
Portugal
Keywords

Time series, Derivative approximation, Fuzzy modelling

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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