Multi-step Ahead Forecasting Using Cartesian Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @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