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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| Published in: | Neurocomputing (Amsterdam) Vol. 127; pp. 161 - 171 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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15.03.2014
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Author | Ragulskis, Minvydas Palivonaite, Rita |
| Author_xml | – sequence: 1 givenname: Rita surname: Palivonaite fullname: Palivonaite, Rita email: rita.palivonaite@ktu.lt organization: Research Group for Mathematical and Numerical Analysis of Dynamical Systems, Kaunas University of Technology, Studentu 50-325, Kaunas LT-51368, Lithuania – sequence: 2 givenname: Minvydas orcidid: 0000-0002-3348-9717 surname: Ragulskis fullname: Ragulskis, Minvydas email: minvydas.ragulskis@ktu.lt organization: Research Group for Mathematical and Numerical Analysis of Dynamical Systems, Kaunas University of Technology, Studentu 50-222, Kaunas LT-51368, Lithuania |
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| Cites_doi | 10.1109/TPAS.1971.293123 10.1002/for.918 10.1016/j.epsr.2006.09.022 10.1016/j.neucom.2011.02.017 10.1016/j.epsr.2007.12.001 10.1016/j.neucom.2009.07.005 10.1016/j.asoc.2009.10.004 10.1007/BF01386381 10.1016/j.jup.2007.10.002 10.1016/S0020-0190(02)00447-7 10.1137/S0036144595295284 10.1016/j.ijforecast.2006.03.005 10.1017/S0962492900002932 10.1016/j.asoc.2006.03.004 10.1093/bioinformatics/bti1022 10.1007/BFb0040810 10.1109/TNN.2006.885113 10.1016/j.dss.2009.07.014 10.1109/TPWRS.2008.2008606 10.1016/S0952-1976(97)00069-9 10.1016/j.neucom.2010.02.014 10.1007/s11771-008-0479-8 |
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| Keywords | 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 |
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| Snippet | 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... |
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| SubjectTerms | 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 |
| Title | Short-term time series algebraic forecasting with internal smoothing |
| URI | https://dx.doi.org/10.1016/j.neucom.2013.08.025 https://www.proquest.com/docview/1506349412 |
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