Short-term time series algebraic forecasting with internal smoothing

A new algebraic forecasting method with internal smoothing is proposed for short-term time series prediction. The concept of the H-rank of a sequence is exploited for the detection of a base algebraic fragment of the time series. Evolutionary algorithms are exploited for the identification of the se...

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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 127; S. 161 - 171
Hauptverfasser: Palivonaite, Rita, Ragulskis, Minvydas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 15.03.2014
Elsevier
Schlagworte:
Algebra
Algebraic sequence
Evolutionary algorithms
Exact sciences and technology
Hankel matrix
Inference from stochastic processes; time series analysis
Mathematics
Probability and statistics
Sciences and techniques of general use
Statistics
Time series forecasting
Hankel matrix
Time series forecasting
Algebraic sequence
Evolutionary algorithms
Short term
Averaging method
Smoothing methods
Evolutionary algorithm
Variability
Time series
Real time
Forecasting
Sequential analysis
Smoothing
Algebraic method
Occupation time
Potential method
ISSN:0925-2312, 1872-8286
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Zusammenfassung:A new algebraic forecasting method with internal smoothing is proposed for short-term time series prediction. The concept of the H-rank of a sequence is exploited for the detection of a base algebraic fragment of the time series. Evolutionary algorithms are exploited for the identification of the set of corrections which are used to perturb the original time series. The proposed forecasting method is constructed to find a near-optimal balance between the variability of algebraic predictors and the smoothness of averaging methods. Numerical experiments with an artificially generated and real-world time series are used to illustrate the potential of the proposed method. •A new algebraic forecasting method with internal smoothing is proposed for short-term time series prediction.•The concept of the H-rank of a sequence is exploited for the detection of a base algebraic fragment of the time series.•The proposed forecasting method is constructed to find a near-optimal balance between the variability of algebraic predictors and the smoothness of averaging methods.
Bibliographie:ObjectType-Article-2
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ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2013.08.025