Imperial College London
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ProfessorNiallAdams

Faculty of Natural Sciences, Department of Mathematics

Professor of Statistics
 
 
 
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Contact

 

+44 (0)20 7594 8837n.adams Website

 
 
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Location

 

6M55Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Plasse:2021:10.1007/s10618-021-00747-7,
author = {Plasse, J and Helfer, Hoeltgebaum H and Adams, N},
doi = {10.1007/s10618-021-00747-7},
journal = {Data Mining and Knowledge Discovery},
pages = {1287--1316},
title = {Streaming changepoint detection for transition matrices},
url = {http://dx.doi.org/10.1007/s10618-021-00747-7},
volume = {35},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Sequentially detecting multiple changepoints in a data stream is a challenging task. Difficulties relate to both computational and statistical aspects, and in the latter, specifying control parameters is a particular problem. Choosing control parameters typically relies on unrealistic assumptions, such as the distributions generating the data, and their parameters, being known. This is implausible in the streaming paradigm, where several changepoints will exist. Further, current literature is mostly concerned with streams of continuous-valued observations, and focuses on detecting a single changepoint. There is a dearth of literature dedicated to detecting multiple changepoints in transition matrices, which arise from a sequence of discrete states. This paper makes the following contributions: a complete framework is developed for adaptively and sequentially estimating a Markov transition matrix in the streaming data setting. A change detection method is then developed, using a novel moment matching technique, which can effectively monitor for multiple changepoints in a transition matrix. This adaptive detection and estimation procedure for transition matrices, referred to as ADEPT-M, is compared to several change detectors on synthetic data streams, and is implemented on two real-world data streams – one consisting of over nine million HTTP web requests, and the other being a well-studied electricity market data set.
AU  - Plasse,J
AU  - Helfer,Hoeltgebaum H
AU  - Adams,N
DO  - 10.1007/s10618-021-00747-7
EP  - 1316
PY  - 2021///
SN  - 1384-5810
SP  - 1287
TI  - Streaming changepoint detection for transition matrices
T2  - Data Mining and Knowledge Discovery
UR  - http://dx.doi.org/10.1007/s10618-021-00747-7
UR  - https://link.springer.com/article/10.1007/s10618-021-00747-7
UR  - http://hdl.handle.net/10044/1/88480
VL  - 35
ER  -
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