  Changepoint detection, a technique for identifying significant shifts within
data sequences, is crucial in various fields such as finance, genomics,
medicine, etc. Dynamic programming changepoint detection algorithms are
employed to identify the locations of changepoints within a sequence, which
rely on a penalty parameter to regulate the number of changepoints. To estimate
this penalty parameter, previous work uses simple models such as linear models
or decision trees. This study introduces a novel deep learning method for
predicting penalty parameters, leading to demonstrably improved changepoint
detection accuracy on large benchmark supervised labeled datasets compared to
previous methods.
