caa a22 4500 606160086 CHVBK 20210128100628.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00521-015-1845-8 doi (NATIONALLICENCE)springer-10.1007/s00521-015-1845-8 Predictive clustering on non-successive observations for multi-step ahead chaotic time series prediction [Elektronische Daten] [V. Gromov, E. Borisenko] Predictive clustering algorithm based upon modified Wishart clustering technique is applied to predict chaotic time series. Concept of predictable and non-predictable observations is introduced in order to distinguish between reliable and unreliable predictions and, consequently, to enhance an ability to predict up to considerable number of positions ahead. Non-predictable observations are easily ascertained in the frameworks of predictive clustering, regardless used clustering technique. Clustering vectors are composed from observations according to set of patterns of non-successive positions in order to reveal characteristic observations sequences, useful for multi-step ahead predictions. The employed clustering method is featured with an ability to generate just enough clusters (submodels) to cope with inherent complexity of the series in question. The methods demonstrate good prediction quality for Lorenz system time series and satisfactory results for weather, energy market and financial time series. The Natural Computing Applications Forum, 2015 Chaotic time series nationallicence Multi-step ahead prediction nationallicence Predictive clustering nationallicence Predictable and non-predictable observations nationallicence Gromov V. Oles Honchar Dnepropetrovsk National University, 49050, Dnepropetrovsk, Ukraine aut Borisenko E. Oles Honchar Dnepropetrovsk National University, 49050, Dnepropetrovsk, Ukraine aut Neural Computing and Applications Springer London 26/8(2015-11-01), 1827-1838 0941-0643 26:8<1827 2015 26 521 https://doi.org/10.1007/s00521-015-1845-8 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-015-1845-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Gromov V. Oles Honchar Dnepropetrovsk National University, 49050, Dnepropetrovsk, Ukraine aut NATIONALLICENCE 700 1- Borisenko E. Oles Honchar Dnepropetrovsk National University, 49050, Dnepropetrovsk, Ukraine aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/8(2015-11-01), 1827-1838 0941-0643 26:8<1827 2015 26 521