Work

Efficient sparsity adaptive changepoint estimation

Year: 2024

Type: article

Abstract: We propose a computationally efficient and sparsity adaptive procedure for estimating changes in unknown subsets of a high-dimensional data sequence. Assuming the data sequence is Gaussian, we prove that the new method successfully estimates the number and locations of changepoints with a given error rate and under minimal conditions for all sparsities of the changing subset. Our method has computational complexity linear up to logarithmic factors in both the length and number of time series, making it applicable to large data sets. Through extensive numerical studies we show that the new methodology is highly competitive in terms of both estimation accuracy and computational cost. The practical usefulness of the method is illustrated by analysing sensor data from a hydro power plant, and an efficient R implementation is available.

Source: Electronic Journal of Statistics

DOI: 10.1214/24-ejs2294

Authors: Per August Jarval Moen, Ingrid Kristine Glad, Martin Tveten

Institutions: University of Oslo, Norwegian Computing Center

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $675

Funder: Norges Forskningsråd