Locally stationary Energy Time Series (LETS)
Lead Research Organisation:
Lancaster University
Department Name: Mathematics and Statistics
Abstract
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People |
ORCID iD |
| Idris Eckley (Principal Investigator) |
Publications
Eckley I
(2014)
Spectral correction for locally stationary Shannon wavelet processes
in Electronic Journal of Statistics
Eckley I
(2018)
A test for the absence of aliasing or local white noise in locally stationary wavelet time series
in Biometrika
Haynes K
(2017)
Computationally Efficient Changepoint Detection for a Range of Penalties
in Journal of Computational and Graphical Statistics
Haynes K
(2017)
A computationally efficient nonparametric approach for changepoint detection.
in Statistics and computing
Killick R
(2012)
Optimal Detection of Changepoints With a Linear Computational Cost
in Journal of the American Statistical Association
Killick R
(2020)
The local partial autocorrelation function and some applications
in Electronic Journal of Statistics
Krzemieniewska K
(2014)
Classification of non-stationary time series
in Stat
Nam C
(2015)
The Uncertainty of Storm Season Changes: Quantifying the Uncertainty of Autocovariance Changepoints
in Technometrics
Park T
(2014)
Estimating Time-Evolving Partial Coherence Between Signals via Multivariate Locally Stationary Wavelet Processes
in IEEE Transactions on Signal Processing
Park T
(2018)
Dynamic classification using multivariate locally stationary wavelet processes
in Signal Processing
| Description | This grant developed new methodology for the modelling and analysis of data using locally stationary time series. This included new ways of assessing the sampling rate (and detecting the absence of aliasing) to new methods for forecasting locally stationary time series. The grant also developed a number of postdocs who have gone onto academic positions |
| Exploitation Route | The majority of the core methods developed as part of this grant have, or are in the process of, being made available in the form of fully-documented. open source, reproducible software packages. |
| Sectors | Aerospace, Defence and Marine,Agriculture, Food and Drink,Chemicals,Communities and Social Services/Policy,Construction,Creative Economy,Digital/Communication/Information Technologies (including Software),Education,Electronics,Energy,Environment,Financial Services, and Management Consultancy,Healthcare,Leisure Activities, including Sports, Recreation and Tourism,Government, Democracy and Justice,Manufacturing, including Industrial Biotechology,Culture, Heritage, Museums and Collections |
| Description | To date, the complete suite of methods has predominantly attracted interest from our industrial partners. At the time of writing, not all results have been published so the full extent of the impact of this grant will not be known for some time. However one noteworthy example of impact arises from the work reported in Killick, Fearnhead and Eckley (2012) that introduces the PELT algorithm to efficiently search for multiple changepoints in a time series. Since publication, the method has been widely adopted across a range of different disciplines and commercial sectors, as well as being implemented in two scientific programming environments (NAG, Matlab). |
| First Year Of Impact | 2015 |
| Sector | Aerospace, Defence and Marine,Agriculture, Food and Drink,Chemicals,Digital/Communication/Information Technologies (including Software),Energy,Environment,Financial Services, and Management Consultancy,Healthcare,Manufacturing, including Industrial Biotechology,Security and Diplomacy,Transport |
| Impact Types | Economic |
| Description | EPSRC Programme Grant |
| Amount | £2,750,890 (GBP) |
| Funding ID | EP/N031938/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 06/2016 |
| End | 05/2022 |
| Title | changepoint package |
| Description | This is an R package for changepoint analysis. The package implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data. Many popular non-parametric and frequentist methods are included, and in particular implements the PELT algorithm described in Killick, R., Fearnhead, P., Eckley, I.A. (2012) Optimal detection of changepoints with a linear computational cost. Journal of the American Statistical Association 107(500) 1590-1598. |
| Type Of Technology | Software |
| Year Produced | 2012 |
| Open Source License? | Yes |
| Impact | The software package has been used by researchers in several other disciplines (including environmental sciences, electrical engineering, linguistics) and several non-academic organizations (including Amazon, GSK, and the UK Office for National Statistics). to derive insight and understanding. |
| URL | http://cran.r-project.org/web/packages/changepoint/index.html |
| Title | mvLSW |
| Description | Tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivariate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence. |
| Type Of Technology | Software |
| Year Produced | 2017 |
| Open Source License? | Yes |
| Impact | Too early |
| URL | https://cran.r-project.org/web/packages/mvLSW/index.html |