Multi-step Ahead Forecasting Using Cartesian Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{Dzalbs:2017:miller,

• author = "Ivars Dzalbs and Tatiana Kalganova",
• title = "Multi-step Ahead Forecasting Using Cartesian Genetic Programming",
• booktitle = "Inspired by Nature: Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday",
• publisher = "Springer",
• year = "2017",
• editor = "Susan Stepney and Andrew Adamatzky",
• volume = "28",
• series = "Emergence, Complexity and Computation",
• chapter = "11",
• pages = "235--246",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming",
• isbn13 = "978-3-319-67996-9",
• DOI = "doi:10.1007/978-3-319-67997-6_11",
• abstract = "This 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. The Cartesian Genetic Programming (CGP) algorithm finds the best performing machine out of a huge abundance of candidates via evolutionary strategy. The algorithm can cope with non-stationary highly multivariate data series, and 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.",
• notes = "part of \cite{miller60book} https://link.springer.com/bookseries/10624",

}

Genetic Programming entries for Ivars Dzalbs Tatiana Kalganova

Citations