  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.
