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

TL;DR

This paper systematically reviews deep-learning algorithms for analyzing complex irregular sequential data, highlighting the dominance of RNNs, common evaluation metrics, and key challenges like robustness and transparency that need addressing.

Contribution

It provides a comprehensive review of existing deep-learning approaches for irregular sequential data and identifies critical challenges to guide future research.

Findings

Recurrent neural networks dominate current frameworks

Evaluation mainly uses mean absolute error and root mean square error

Key challenges include lack of robustness and transparency

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.