Gaetano Romano

Gaetano Romano
Lancaster University | LU · Department of Mathematics and Statistics

Doctor of Philosophy

About

6
Publications
442
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22
Citations
Citations since 2016
6 Research Items
22 Citations
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Introduction
My research focus on fast and efficient changepoint and anomaly detection for large data streams. I have been developing novel dynamical programming algorithms capable of dealing with applications where the usual normality assumptions fall. Lately, I have been looking at online anomaly detection algorithms.

Publications

Publications (6)
Article
Motivated by a telecommunications application where there are few computational constraints, a novel nonparametric algorithm, NUNC, is introduced to perform an online detection for changes in the distribution of data. Two variants are considered: the first, NUNC Local, detects changes within a sliding window. Conversely, NUNC Global, compares the c...
Preprint
Full-text available
Many modern applications of online changepoint detection require the ability to process high-frequency observations, sometimes with limited available computational resources. Online algorithms for detecting a change in mean often involve using a moving window, or specifying the expected size of change. Such choices affect which changes the algorith...
Article
Full-text available
Whilst there are a plethora of algorithms for detecting changes in mean in univariate time-series, almost all struggle in real applications where there is autocorrelated noise or where the mean fluctuates locally between the abrupt changes that one wishes to detect. In these cases, default implementations, which are often based on assumptions of a...
Article
Applying digestate, the residue from anaerobic digestion, to soil as a replacement for inorganic fertiliser is of growing interest in agriculture. However, the impacts of different fractions of digestate on the soil carbon (C) cycle remain unclear and provide the focus for the research reported here. We examined the effects of applying whole digest...
Preprint
Full-text available
Whilst there are a plethora of algorithms for detecting changes in mean in univariate time-series, almost all struggle in real applications where there is autocorrelated noise or where the mean fluctuates locally between the abrupt changes that one wishes to detect. In these cases, default implementations, which are often based on assumptions of a...
Preprint
Full-text available
In a world with data that change rapidly and abruptly, it is important to detect those changes accurately. In this paper we describe an R package implementing an algorithm recently proposed by Hocking et al. [2017] for penalised maximum likelihood inference of constrained multiple change-point models. This algorithm can be used to pinpoint the prec...

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