Research

My research interests lie in statistics and operational research, more specifically in time series analysis with a focus on applications to health data. My research thus far has focused on the development and use of functional analysis for detecting outliers in time series data. We have performed case studies on multiple empirical data sets including railway booking data and bike-sharing usage data.

A blog post giving an overview of my PhD thesis is available on my website.

Publications

Analysing and visualising bike sharing demand with outliers.
N. Rennie, C. Cleophas, A. M. Syksulski, F. Dost. 2023.
Discover Data. Volume 1, Issue 1. March 2023.
doi: doi.org/10.1007/s44248-023-00001-z

Outlier detection in network revenue management.
N. Rennie, C. Cleophas, A. M. Syksulski, F. Dost. 2023.
Accepted in OR Spectrum.
Pre-print: arxiv.org/abs/2104.04157.

We’re not getting any younger! Or should that be older?
N. Rennie.
Significance (online). 2022.
Link: www.significancemagazine.com/science/723-we-re-not-getting-any-younger-or-should-that-be-older.

Detecting demand outliers in transport systems
N. Rennie.
PhD Thesis. 2021.
doi: doi.org/10.17635/lancaster/thesis/1448.

Identifying and responding to outlier demand in revenue management.
N. Rennie, C. Cleophas, A. M. Syksulski, F. Dost.
European Journal of Operational Research. Volume 293, Issue 3, 16 September 2021, 1015-1030.
doi: doi.org/10.1016/j.ejor.2021.01.002.