1 citations · 2 across the 4 of their papers we have counts for
5 papers
Multiple change-point detection via bottom-up scanning
Jinhyeok Park, Hyeyoung Maeng, Hoseung Song
We study nonparametric multiple change-point detection for high-dimensional sequences, aiming to identify time points at which the underlying distribution changes. While many exist…
High-dimensional sparsity-adaptive multiple change-point detection
Hyeyoung Maeng, Tengyao Wang, Piotr Fryzlewicz
We introduce a method for detecting multiple change-points in the mean of a high-dimensional data sequence. Unlike existing top-down (i.e. divisive) algorithms, we adopt a bottom-u…
High-dimensional time series segmentation via factor-adjusted vector autoregressive modelling
Haeran Cho, Hyeyoung Maeng, Idris A. Eckley +1
Vector autoregressive (VAR) models are popularly adopted for modelling high-dimensional time series, and their piecewise extensions allow for structural changes in the data. In VAR…
Collective anomaly detection in High-dimensional VAR Models
Hyeyoung Maeng, Idris Eckley, Paul Fearnhead
There is increasing interest in detecting collective anomalies: potentially short periods of time where the features of data change before reverting back to normal behaviour. We pr…
Detecting linear trend changes in data sequences
Hyeyoung Maeng, Piotr Fryzlewicz
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 ne…