  This paper provides a review of past approaches to the use of deep-learning
frameworks for the analysis of discrete irregular-patterned 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. The performance
of deep-learning frameworks was found to be evaluated mainly using mean
absolute error and root mean square error accuracy metrics. Underlying
challenges that were identified are: lack of performance robustness,
non-transparency of the methodology, internal and external architectural design
and configuration issues. These challenges provide an opportunity to improve
the framework for complex irregular-patterned sequential datasets.
