Gaetano RomanoLancaster University | LU · Department of Mathematics and Statistics
Gaetano Romano
Doctor of Philosophy
About
13
Publications
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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 (13)
![FOCuS update at time T for θ1>θ0\documentclass[12pt]{minimal}...](publication/379080816/figure/fig1/AS:11431281247274644@1716689500169/FOCuS-update-at-time-T-for-th1th0documentclass12ptminimal-usepackageamsmath_Q320.jpg)
![FOCuS maxima check at time T for θ1≥θ0\documentclass[12pt]{minimal}...](publication/379080816/figure/fig2/AS:11431281247274645@1716689500383/FOCuS-maxima-check-at-time-T-for-th1th0documentclass12ptminimal-usepackageamsmath_Q320.jpg)
Online changepoint detection algorithms that are based on (generalised) likelihood-ratio tests have been shown to have excellent statistical properties. However, a simple online implementation is computationally infeasible as, at time T, it involves considering O(T) possible locations for the change. Recently, the FOCuS algorithm has been introduce...
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 a generalized version of an algorithm recently proposed by Hocking, Rigaill, Fearnhead, and Bourque (2020) for penalized maximum likelihood inference of constrained multiple change-point mode...
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...
Online changepoint detection algorithms that are based on likelihood-ratio tests have been shown to have excellent statistical properties. However, a simple online implementation is computationally infeasible as, at time $T$, it involves considering $O(T)$ possible locations for the change. Recently, the FOCuS algorithm has been introduced for dete...
Online changepoint detection aims to detect anomalies and changes in real-time in high-frequency data streams, sometimes with limited available computational resources. This is an important task that is rooted in many real-world applications, including and not limited to cybersecurity, medicine and astrophysics. While fast and efficient online algo...
Online changepoint detection aims to detect anomalies and changes in real-time in high-frequency data streams, sometimes with limited available computational resources. This is an important task that is rooted in many real-world applications, including and not limited to cybersecurity, medicine and astrophysics. While fast and efficient online algo...
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...
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...
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...
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...
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...
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...
