Complex Sequential Data Analysis: A Systematic Literature Review of Existing Algorithms

• URL: http://arxiv.org/abs/2007.11572v1
• Date: Wed, 22 Jul 2020 17:53:00 GMT
• Title: Complex Sequential Data Analysis: A Systematic Literature Review of Existing Algorithms
• Authors: Kudakwashe Dandajena, Isabella M. Venter, Mehrdad Ghaziasgar and Reg Dodds
• Abstract summary: This paper reviews past approaches to the use of deep-learning frameworks for the analysis of irregular-patterned datasets. Traditional deep-learning methods perform poorly or even fail when trying to analyse these datasets. The performance of deep-learning frameworks was found to be evaluated mainly using mean absolute error and root mean square error accuracy metrics.
• Score: 0.9649642656207869
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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