| dc.contributor.advisor | Ohm Sornil | th |
| dc.contributor.author | Touchpakorn Dhammathanapatchara | th |
| dc.date.accessioned | 2023-01-24T08:07:40Z | |
| dc.date.available | 2023-01-24T08:07:40Z | |
| dc.date.issued | 2015 | |
| dc.identifier.other | b191162 | th |
| dc.identifier.uri | https://repository.nida.ac.th/handle/662723737/6244 | |
| dc.description | Thesis (M.S. (Computer Science and Information Systems))--National Institute of Development Administration, 2015 | th |
| dc.description.abstract | The technical analysis of stocks and trends has been used by traders for decades to predict a particular market movement. The candlestick chart pattern analysis is a widely-used technical analysis technique. With a large number of patterns, manually identifying patterns from a price chart has been found to be
difficult; thus an automatic means is needed to aid investors. In this research the candlestick chart pattern recognition technique is discussed, which can effectively identify candlestick chart patterns and their evaluation. | th |
| dc.description.provenance | Submitted by นักศึกษาฝึกงานมหาวิทยาลัยทักษิณ (2566) (บุษกร แก้วพิทักษ์คุณ) (budsak.a@nida.ac.th) on 2023-01-24T08:07:40Z
No. of bitstreams: 1
b191162.pdf: 7152150 bytes, checksum: a89930c3c2257cc25083c6be83680a2c (MD5) | en |
| dc.description.provenance | Made available in DSpace on 2023-01-24T08:07:40Z (GMT). No. of bitstreams: 1
b191162.pdf: 7152150 bytes, checksum: a89930c3c2257cc25083c6be83680a2c (MD5)
Previous issue date: 2015 | en |
| dc.format.extent | 44 leaves | th |
| dc.format.mimetype | application/pdf | th |
| dc.language.iso | eng | th |
| dc.publisher | National Institute of Development Administration | th |
| dc.subject.other | Technical analysis | th |
| dc.subject.other | Candlestick chart | th |
| dc.title | Candlestick chart pattern recognition | th |
| dc.type | Text | th |
| mods.genre | Thesis | th |
| mods.physicalLocation | National Institute of Development Administration. Library and Information Center | th |
| thesis.degree.name | Master of Science | th |
| thesis.degree.level | Master's | th |
| thesis.degree.discipline | Computer Science and Information Systems | th |
| thesis.degree.grantor | National Institute of Development Administration | th |
| thesis.degree.department | Graduate School of Applied Statistics | th |
| dc.identifier.doi | 10.14457/NIDA.the.2015.114 | |
