Complex Sequential Data Analysis: A Systematic Literature Review of Existing Algorithms
It addresses the problem of poor performance of traditional deep-learning methods on irregular sequential data for researchers and practitioners, but it is incremental as it reviews existing work without introducing new methods.
This paper conducted a systematic literature review on deep-learning frameworks for analyzing discrete irregular-patterned complex sequential datasets, such as financial data, and found that recurrent neural networks dominate these approaches, with performance evaluated using metrics like mean absolute error and root mean square error.
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.