264 citations · 864 across the 22 of their papers we have counts for
23 papers
Nonparametric data segmentation in multivariate time series via joint characteristic functions
Euan T. McGonigle, Haeran Cho
Modern time series data often exhibit complex dependence and structural changes which are not easily characterised by shifts in the mean or model parameters. We propose a nonparame…
fnets: An R Package for Network Estimation and Forecasting via Factor-Adjusted VAR Modelling
Dom Owens, Haeran Cho, Matteo Barigozzi
The package fnets for the R language implements the suite of methodologies proposed by Barigozzi et al. (2022) for the network estimation and forecasting of high-dimensional time s…
Capturing usage patterns in bike sharing system via multilayer network fused Lasso
Yunjin Choi, Haeran Cho, Hyelim Son
Data collected from a bike-sharing system exhibit complex temporal and spatial features. We analyze shared-bike usage data collected in three large cities at the level of individua…
Moving sum procedure for change point detection under piecewise linearity
Joonpyo Kim, Hee-Seok Oh, Haeran Cho
We propose a computationally and statistically efficient procedure for segmenting univariate data under piecewise linearity. The proposed moving sum (MOSUM) methodology detects mul…
Robust multiscale estimation of time-average variance for time series segmentation
Euan T. McGonigle, Haeran Cho
There exist several methods developed for the canonical change point problem of detecting multiple mean shifts, which search for changes over sections of the data at multiple scale…
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…
FNETS: Factor-adjusted network estimation and forecasting for high-dimensional time series
Matteo Barigozzi, Haeran Cho, Dom Owens
We propose FNETS, a methodology for network estimation and forecasting of high-dimensional time series exhibiting strong serial- and cross-sectional correlations. We operate under…
Bootstrap confidence intervals for multiple change points based on moving sum procedures
Haeran Cho, Claudia Kirch
The problem of quantifying uncertainty about the locations of multiple change points by means of confidence intervals is addressed. The asymptotic distribution of the change point…
Data segmentation algorithms: Univariate mean change and beyond
Haeran Cho, Claudia Kirch
Data segmentation a.k.a. multiple change point analysis has received considerable attention due to its importance in time series analysis and signal processing, with applications i…
Multiple change point detection under serial dependence: Wild contrast maximisation and gappy Schwarz algorithm
Haeran Cho, Piotr Fryzlewicz
We propose a methodology for detecting multiple change points in the mean of an otherwise stationary, autocorrelated, linear time series. It combines solution path generation based…
Discussion of 'Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection'
Haeran Cho, Claudia Kirch
We discuss the theoretical guarantee provided by the WBS2.SDLL proposed in Fryzlewicz (2020) and explore an alternative, MOSUM-based candidate generation method for the SDLL.
Two-stage data segmentation permitting multiscale change points, heavy tails and dependence
Haeran Cho, Claudia Kirch
The segmentation of a time series into piecewise stationary segments, a.k.a. multiple change point analysis, is an important problem both in time series analysis and signal process…
Consistent estimation of high-dimensional factor models when the factor number is over-estimated
Matteo Barigozzi, Haeran Cho
A high-dimensional -factor model for an -dimensional vector time series is characterised by the presence of a large eigengap (increasing with ) between the -th and the…
Link prediction for interdisciplinary collaboration via co-authorship network
Haeran Cho, Yi Yu
We analyse the Publication and Research (PURE) data set of University of Bristol collected between and . Using the existing co-authorship network and academic informat…
High-dimensional GARCH process segmentation with an application to Value-at-Risk
Haeran Cho, Karolos Korkas
Models for financial risk often assume that underlying asset returns are stationary. However, there is strong evidence that multivariate financial time series entail changes not on…
Simultaneous multiple change-point and factor analysis for high-dimensional time series
Matteo Barigozzi, Haeran Cho, Piotr Fryzlewicz
We propose the first comprehensive treatment of high-dimensional time series factor models with multiple change-points in their second-order structure. We operate under the most fl…
Multiscale and multilevel technique for consistent segmentation of nonstationary time series
Haeran Cho, Piotr Fryzlewicz
In this paper, we propose a fast, well-performing, and consistent method for segmenting a piecewise-stationary, linear time series with an unknown number of breakpoints. The time s…
High-dimensional variable selection via tilting
Haeran Cho, Piotr Fryzlewicz
The paper considers variable selection in linear regression models where the number of covariates is possibly much larger than the number of observations. High dimensionality of th…
Multiple-change-point detection for high dimensional time series via sparsified binary segmentation
Haeran Cho, Piotr Fryzlewicz
Time series segmentation, a.k.a. multiple change-point detection, is a well-established problem. However, few solutions are designed specifically for high-dimensional situations. I…
A test for second-order stationarity of time series based on unsystematic sub-samples
Haeran Cho
In this paper, we introduce a new method for testing the stationarity of time series, where the test statistic is obtained from measuring and maximising the difference in the secon…
Multiscale interpretation of taut string estimation and its connection to Unbalanced Haar wavelets
Haeran Cho, Piotr Fryzlewicz
We compare two state-of-the-art non-linear techniques for nonparametric function estimation via piecewise constant approximation: the taut string and the Unbalanced Haar methods. W…
Modelling and forecasting daily electricity load curves: a hybrid approach
Haeran Cho, Yannig Goude, Xavier Brossat +1
We propose a hybrid approach for the modelling and the short-term forecasting of electricity loads. Two building blocks of our approach are (i) modelling the overall trend and seas…
Change-point detection in panel data via double CUSUM statistic
Haeran Cho
In this paper, we consider the problem of (multiple) change-point detection in panel data. We propose the double CUSUM statistic which utilises the cross-sectional change-point str…