# Selecting Datasets for Evaluating an Enhanced Deep Learning Framework

arXiv: https://arxiv.org/abs/2109.10442 · HTML: https://mlsanity.com/paper/2109.10442/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Deep Equilibrium Models](https://mlsanity.com/paper/1909.01377/) (2019)
- [Accurate Uncertainties for Deep Learning Using Calibrated Regression](https://mlsanity.com/paper/1807.00263/) (2018)
- [A Memory-Network Based Solution for Multivariate Time-Series Forecasting](https://mlsanity.com/paper/1809.02105/) (2018)
- [Multimodal deep learning for short-term stock volatility prediction](https://mlsanity.com/paper/1812.10479/) (2018)
- [Improved Forecasting of Cryptocurrency Price using Social Signals](https://mlsanity.com/paper/1907.00558/) (2019)
- [Comparison of Deep learning models on time series forecasting : a case study of Dissolved Oxygen Prediction](https://mlsanity.com/paper/1911.08414/) (2019)
- [Complex Sequential Data Analysis: A Systematic Literature Review of Existing Algorithms](https://mlsanity.com/paper/2007.11572/) (2020)

## Abstract (arXiv)

A framework was developed to address limitations associated with existing techniques for analysing sequences. This work deals with the steps followed to select suitable datasets characterised by discrete irregular sequential patterns. To identify, select, explore and evaluate which datasets from various sources extracted from more than 400 research articles, an interquartile range method for outlier calculation and a qualitative Billauer's algorithm was adapted to provide periodical peak detection in such datasets. The developed framework was then tested using the most appropriate datasets. The research concluded that the financial market-daily currency exchange domain is the most suitable kind of data set for the evaluation of the designed deep learning framework, as it provides high levels of discrete irregular patterns.