• Corpus ID: 243848333

Detecting linear trend changes in data sequences

@inproceedings{Maeng2019DetectingLT,
  title={Detecting linear trend changes in data sequences},
  author={Hyeyoung Maeng and Piotr Fryzlewicz},
  year={2019}
}
We propose TrendSegment, a methodology for detecting multiple change-points corresponding to linear trend changes in one dimensional data. A core ingredient of TrendSegment is a new Tail-Greedy Unbalanced Wavelet transform: a conditionally orthonormal, bottomup transformation of the data through an adaptively constructed unbalanced wavelet basis, which results in a sparse representation of the data. Due to its bottom-up nature, this multiscale decomposition focuses on local features in its… 

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