Visualizing stock market data with self‐organizing map
| dc.contributor.author | Joseph, Joel | |
| dc.contributor.author | Indratmo, Indratmo | |
| dc.date.accessioned | 2017-05-10 | |
| dc.date.accessioned | 2022-05-28T00:37:09Z | |
| dc.date.available | 2022-05-28T00:37:09Z | |
| dc.date.issued | 2013 | |
| dc.description.abstract | Finding useful patterns in stock market data requires tremendous analytical skills and effort. To help investors manage their portfolios, we developed a tool for clustering and visualizing stock market data using an unsupervised learning algorithm called Self-Organizing Map. Our tool is intended to assist users in identifying groups of stocks that have similar price movement patterns over a period of time. We performed a visual analysis by comparing the resulting visualization with Yahoo Finance charts. Overall, we found that the Self-Organizing Map algorithm can analyze and cluster the stock market data reasonably. | |
| dc.format.extent | 752.22 KB | |
| dc.format.mimetype | ||
| dc.identifier.citation | Joseph, J. & Indratmo (2013). Visualizing stock market data with self-organizing map. In Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference, St. Pete Beach, Florida, USA, 488–491. Retrieved from https://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS13/paper/view/5937/6123 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14078/863 | |
| dc.language | English | |
| dc.language.iso | en | |
| dc.rights | All Rights Reserved | |
| dc.title | Visualizing stock market data with self‐organizing map | en |
| dc.type | Presentation | |
| dspace.entity.type |
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