- author = "Roy Schwaerzel",
- title = "Financial Time Series Prediction and Evaluation by Genetic Programming with Trigonometric Functions and High-Order Statistics",
- school = "Department of Computer Science, The University of Texas at San Antonio",
- year = "2006",
- address = "USA",
- month = dec,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.298.6260",
-
URL = "
http://www.cs.utsa.edu/uploads/theses/RSchwaerzel.pdf",
-
URL = "https://cs.utsa.edu/research/dissertations?page=3",
-
URL = "https://cs.utsa.edu/node/267",
- size = "139 pages",
- abstract = "This research describes an extension of the traditional application of Genetic Programming in the domain of the prediction of daily currency exchange rates. In combination with trigonometric operators, we introduce a new set of high-order statistical functions in a unique representation and analyse their performance using daily returns of the British Pound and Japanese Yen. In addition, the same experimental design and analysis is applied to ten other financial time series from two different domains. We will demonstrate that the introduction of high-order statistical functions in combination with trigonometric functions will outperform other traditional models such as ARMA models and Genetic Programming with the basic function set. We use the Akaike Information Criterion for the selection of the best ARMA model for our benchmark testing. Performance will be measured on hit percentage, average percentage change, and profit. The t-Test is applied to derive confidence intervals and to evaluate the significance of our results.",
-
notes = "Supervising Professor: Dr. Tom Bylander
'We have shown that the Genetic Programming models outperformed the Akaike selected ARMA model and the simple Buy and Hold Strategy using the HIT, the APC, and the Profit performance measures'
UKP GBP v Yen JPY",
