An abstract image of numbers and blue lines

Changepoints and Time Series

Group Members

Loading People


Google Summer Of Code 2018
01/05/2018 → 31/08/2018

EuroStat: Statistics 4 Beginners
01/01/2018 → 31/12/2019

Industrial CASE Account - Lancaster University 2017
01/10/2017 → …

Studentship iCASE
01/10/2017 → …

DSI:LHOFT - Liverpool-Humber Optimisation of Freight Transport
01/08/2017 → 31/07/2020

Automatic Model Selection:Changepoints, Long Memory and Environmental Data
26/06/2017 → 18/08/2017

DSI: Research related to the production of principal European Economic Indicator (PEEIS)
01/04/2017 → 31/12/2017

DSI - Changepoint Identification for Improving Forecasts
12/09/2016 → 11/12/2016

Statscale: Statistical Scalability for streaming data
01/06/2016 → 31/05/2022

Evolution of seasonal adjustment methods
15/01/2016 → 31/03/2016

Forecasting river levels utilising non stationarity
01/01/2016 → …

Development of solutions to tea production problems
02/11/2015 → 31/08/2017

Detection of abrupt changes in land and ocean ecosystems
05/10/2015 → 31/03/2016

Trace Forecast Likelihood
13/12/2014 → …

Intractable Likelihood: New Challenges From Modern Applications (iLike)
01/01/2013 → 30/06/2018

CDT: STOR-I (Main Project)
01/10/2010 → 31/03/2018

Research Activity

Time series, i.e. data measured over time, arise in many natural and industrial applications.

Examples of such series include historical environmental measurements, financial indicators, and physiological or biological processes. These series are often high-dimensional in nature and exhibit complex temporal characteristics. Being able to analyse the changing structure of such data is key to understanding the dynamics of many important physical processes.

Wavelets and Locally Stationary Time Series

Many of the time series which we generate are characterised by a time-evolving statistical structure, so called locally stationary time series. Failing to account for such realities can result in serious consequences. Our work seeks to develop more realistic models and analysis methods which explicitly account for such time-varying structure, for tasks such as statistical testing, forecasting, and classification.

Changepoint methods

With the increased availability of high frequency data sources, there is a need for algorithms for detecting times when changes in time series occur, particularly in cases where such changes may not be immediately obvious. A particular current focus is hence on the development of accurate and computationally efficient changepoint search methods.

Examples of application areas in which changepoint detection is important include ensuring safety in industrial process monitoring and intrusion detection.

Robust estimation and bootstrapping in financial time series

Our research focuses on the analysis and computation of parameter estimators associated with the volatility models of financial time series such as GARCH model. In particular, we propose estimators that are robust and perform well even under various deviations from the underlying model assumptions. We consider applications of robust estimators to the analysis of financial risk measures. We study both theoretical and empirical properties of these estimators and the bootstrap approximations of their distributions.