- author = "M. A. Kaboudan and M. K. Vance",
- title = "Statistical Evaluation of Symbolic Regression Forecasting of Time-Series",
- booktitle = "Proceedings of the International Federation of Automatic Control Symposium on Computation in Economics, Finance and Engineering: Economic Systems",
- year = "1998",
- editor = "S. Holly",
- volume = "31",
- number = "16",
- pages = "275--279",
- address = "Cambridge, UK",
- month = "29 " # jun # " - 1 " # jul,
- organisation = "IFAC",
- keywords = "genetic algorithms, genetic programming, nonlinear dynamics, complexity, artificial intelligence",
- ISBN = "0-08-043048-1",
- ISSN = "1474-6670",
-
URL = "
http://www.sciencedirect.com/science/article/pii/S1474667017404940",
-
URL = "http://gso.gbv.de/DB=2.1/PPNSET?PPN=318304171",
- broken = "http://catalog.library.colostate.edu/record=b2366049~S5",
-
DOI = "
doi:10.1016/S1474-6670(17)40494-0",
- size = "5 pages",
- abstract = "This is an evaluation of the ability of symbolic regression to predict time series. Symbolic regression is an application of genetic programming. Three codes GPCPP, GPQuick, and Vienna University GP Kemel-written in C++ were tested. Six models generated data by linear, nonlinear, and pseudo-random processes, and the three codes were employed to search for the six data generating processes. The results suggest that: (1) complexity and predictability are inversely related, (2) the symbolic regression technique is successful in predicting less complex processes, and (3) all three failed to find a data generating process for pseudo-random data.",
-
notes = "IFAC Symposium. Published for the International
Federation of Automatic Control by Pergamon, 2000.
Also known as \cite{KABOUDAN1998275}",
