Complex sequential data analysis: A systematic literature review of existing algorithms
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Date
2020Author
Dandajena, Kudakwashe
Venter, Isabella M.
Ghaziasgar, Mehrdad
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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.