Improving Classification of Patterns in Ultra-High Frequency Time Series with Evolutionary Algorithms
| Title | Improving Classification of Patterns in Ultra-High Frequency Time Series with Evolutionary Algorithms |
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Authors | Lipinski, Piotr, Michalak, Krzysztof, Lancucki, Adrian |
| Conference Name | Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4323-7 |
| Keywords | differential evolution, imbalanced classification, Metrics, order book shapes, pubcrawl, Resiliency, Scalability, security, Support vector machines, Time Frequency Analysis, ultra-high frequency financial time series |
| Abstract | This paper proposes a method of distinguishing stock market states, classifying them based on price variations of securities, and using an evolutionary algorithm for improving the quality of classification. The data represents buy/sell order queues obtained from rebuild order book, given as price-volume pairs. In order to put more emphasis on certain features before the classifier is used, we use a weighting scheme, further optimized by an evolutionary algorithm. |
| URL | http://doi.acm.org/10.1145/2908961.2909042 |
| DOI | 10.1145/2908961.2909042 |
| Citation Key | lipinski_improving_2016 |