Complex sequential data analysis: A systematic literature review of existing algorithms
| dc.contributor.author | Dandajena, Kudakwashe | |
| dc.contributor.author | Venter, Isabella M. | |
| dc.contributor.author | Ghaziasgar, Mehrdad | |
| dc.date.accessioned | 2020-12-10T07:55:36Z | |
| dc.date.available | 2020-12-10T07:55:36Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | This paper provides a review of past approaches to the use of deep-learning frameworks for the analysis of discrete irregularpatterned complex sequential datasets. A typical example of such a dataset is financial data where specific events trigger sudden irregular changes in the sequence of the data. Traditional deep-learning methods perform poorly or even fail when trying to analyse these datasets. The results of a systematic literature review reveal the dominance of frameworks based on recurrent neural networks. | en_US |
| dc.identifier.citation | Dandajena, K . et al. (2020).Complex sequential data analysis: A systematic literature review of existing algorithms. ACM international conference proceeding series, Cape Town, 44-50 | en_US |
| dc.identifier.uri | https://doi.org/10.1145/3410886.3410899 | |
| dc.identifier.uri | http://hdl.handle.net/10566/5486 | |
| dc.language.iso | en | en_US |
| dc.publisher | SAICSIT | en_US |
| dc.subject | Irregular patterns | en_US |
| dc.subject | Time series forecasting | en_US |
| dc.subject | Parameter | en_US |
| dc.subject | Volatile financial prediction | en_US |
| dc.subject | State-of-the-art | en_US |
| dc.title | Complex sequential data analysis: A systematic literature review of existing algorithms | en_US |
| dc.type | Presentation | en_US |