Dandajena, KudakwasheVenter, Isabella M.Ghaziasgar, Mehrdad2020-12-102020-12-102020Dandajena, K . et al. (2020).Complex sequential data analysis: A systematic literature review of existing algorithms. ACM international conference proceeding series, Cape Town, 44-50https://doi.org/10.1145/3410886.3410899http://hdl.handle.net/10566/5486This 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.enIrregular patternsTime series forecastingParameterVolatile financial predictionState-of-the-artPresentation