Editing Burrows–Wheeler transform (section)

===BWT for sequence prediction===
BWT has also been proved to be useful on sequence prediction which is a common area of study in [[machine learning]] and [[natural-language processing]]. In particular, Ktistakis et al.<ref name="Ktistakis, R2019">{{cite book |vauthors= Ktistakis R, Fournier-Viger P, Puglisi SJ, Raman R|chapter=Succinct BWT-Based Sequence Prediction |chapter-url=https://figshare.com/articles/conference_contribution/Succinct_BWT-based_Sequence_prediction/10200137| title=  Database and Expert Systems Applications |series=Lecture Notes in Computer Science |date= 2019 |volume= 11707 |issue=10 |pages= 91–101 | doi=10.1007/978-3-030-27618-8_7|isbn=978-3-030-27617-1 |s2cid=201058996 }}</ref> proposed a sequence prediction scheme called SuBSeq that exploits the lossless compression of data of the Burrows–Wheeler transform. SuBSeq exploits BWT by extracting the [[FM-index]] and then performing a series of operations called backwardSearch, forwardSearch, neighbourExpansion, and getConsequents in order to search for predictions given a [[suffix]]. The predictions are then classified based on a weight and put into an array from which the element with the highest weight is given as the prediction from the SuBSeq algorithm. SuBSeq has been shown to outperform [[state of the art]] algorithms for sequence prediction both in terms of training time and accuracy.