%0 Conference Paper %1 chen:1997:GPmvfts %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %B Genetic Programming 1997: Proceedings of the Second Annual Conference %C Stanford University, CA, USA %D 1997 %E Koza, John R. %E Deb, Kalyanmoy %E Dorigo, Marco %E Fogel, David B. %E Garzon, Max %E Iba, Hitoshi %E Riolo, Rick L. %I Morgan Kaufmann %K algorithms, genetic programming %P 58--63 %T Using Genetic Programming to Model Volatility in Financial Time Series %X RGP tested by using Nikkei 255 and S&P 500 as an example %@ 1-55860-483-9

@inproceedings{chen:1997:GPmvfts, abstract = {RGP tested by using Nikkei 255 and S&P 500 as an example}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Stanford University, CA, USA}, author = {Chen, Shu-Heng and Yeh, Chia-Hsuan}, biburl = {https://www.bibsonomy.org/bibtex/2143e5accfa01f349d05e1b4d8074b5c3/brazovayeye}, booktitle = {Genetic Programming 1997: Proceedings of the Second Annual Conference}, editor = {Koza, John R. and Deb, Kalyanmoy and Dorigo, Marco and Fogel, David B. and Garzon, Max and Iba, Hitoshi and Riolo, Rick L.}, interhash = {708a6adafc0e54d09911217d2f6f3de7}, intrahash = {143e5accfa01f349d05e1b4d8074b5c3}, isbn = {1-55860-483-9}, keywords = {algorithms, genetic programming}, month = {13-16 July}, notes = {GP-97 Fixed size sliding window of the original time series. BGP used to learn first window, then whole pop used with second window (ie as population seed). Fitness = sum of errors squared also serves to give estimate of volatility.}, pages = {58--63}, publisher = {Morgan Kaufmann}, publisher_address = {San Francisco, CA, USA}, timestamp = {2008-06-19T17:37:42.000+0200}, title = {Using Genetic Programming to Model Volatility in Financial Time Series}, year = 1997 }