Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/15134
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dc.contributor.authorKalganova, T-
dc.contributor.authorDzalbs, I-
dc.date.accessioned2017-09-11T14:06:10Z-
dc.date.available2017-09-11T14:06:10Z-
dc.date.issued2017-
dc.identifier.citationInspired by Nature - Essays Presented to Julian Miller on the Occasion of his 60th Birthdayen_US
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/15134-
dc.description.abstractThis paper describes a forecasting method that is suitable for long range predictions. Forecasts are made by a calculating machine of which inputs are the actual data and the outputs are the forecasted values. Furthermore, it is the Cartesian Genetic Programming (CGP) algorithm that finds the best performing machine out of huge abundance of candidates via evolutionary strategy. The algorithm can cope with non-stationary highly multivariate data series, and it can reveal hidden relationships among the input variables. Multiple experiments were devised by looking at several time series from different industries. Forecast results were analysed and compared using average Symmetric Mean Absolute Percentage Error (SMAPE) across all datasets. Overall, CGP achieved comparable to Support Vector Machine algorithm and performed better than Neural Networks.en_US
dc.description.sponsorshipAuthors would like to thank the supporter of this work: Intel Corporation.en_US
dc.language.isoenen_US
dc.subjectCartesian Genetic Programmingen_US
dc.subjectForecasting methoden_US
dc.titleMulti-step ahead forecasting using Cartesian Genetic Programmingen_US
dc.typeArticleen_US
dc.relation.isPartOfInspired by Nature - Essays Presented to Julian Miller on the Occasion of his 60th Birthday-
pubs.publication-statusAccepted-
Appears in Collections:Dept of Electronic and Computer Engineering Embargoed Research Papers

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