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

arXiv: https://arxiv.org/abs/2007.11572 · HTML: https://mlsanity.com/paper/2007.11572/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Selecting Datasets for Evaluating an Enhanced Deep Learning Framework](https://mlsanity.com/paper/2109.10442/) (2021)

## Abstract (arXiv)

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.