Complex sequential data analysis: A systematic literature review of existing algorithms
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
SAICSIT
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.
Description
Keywords
Irregular patterns, Time series forecasting, Parameter, Volatile financial prediction, State-of-the-art
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