Outputs (2)

High-Dimensional Time Series Segmentation via Factor-Adjusted Vector Autoregressive Modeling (2023)
Journal Article
Cho, H., Maeng, H., Eckley, I. A., & Fearnhead, P. (2023). High-Dimensional Time Series Segmentation via Factor-Adjusted Vector Autoregressive Modeling. Journal of the American Statistical Association, https://doi.org/10.1080/01621459.2023.2240054

Vector autoregressive (VAR) models are popularly adopted for modeling high-dimensional time series, and their piecewise extensions allow for structural changes in the data. In VAR modeling, the number of parameters grow quadratically with the dimensi... Read More about High-Dimensional Time Series Segmentation via Factor-Adjusted Vector Autoregressive Modeling.

Detecting linear trend changes in data sequences (2023)
Journal Article
Maeng, H., & Fryzlewicz, P. (2024). Detecting linear trend changes in data sequences. Statistical Papers, 65(3), 1645-1675. https://doi.org/10.1007/s00362-023-01458-5

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 orthonorma... Read More about Detecting linear trend changes in data sequences.