Online Change-point Detection Research Articles

Online learning of order flow and market impact with Bayesian change-point detection methods

Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can be identified through suitable time series models applied to market data. In this paper, we propose the use of Bayesian online change-point detection (BOCPD) methods to identify regime shifts in real-time and enable online predictions of order flow and market impact. To enhance the effectiveness of our approach, we have developed a novel BOCPD method using a score-driven approach. This method accommodates temporal correlations and time-varying parameters within each regime. Through empirical application to NASDAQ data, we have found that: (i) Our newly proposed model demonstrates superior out-of-sample predictive performance compared to existing models that assume i.i.d. behavior within each regime; (ii) When examining the residuals, our model demonstrates good specification in terms of both distributional assumptions and temporal correlations; (iii) Within a given regime, the price dynamics exhibit a concave relationship with respect to time and volume, mirroring the characteristics of actual large orders; (iv) By incorporating regime information, our model produces more accurate online predictions of order flow and market impact compared to models that do not consider regimes.

A communication-efficient, online changepoint detection method for monitoring distributed sensor networks

We consider the challenge of efficiently detecting changes within a network of sensors, where we also need to minimise communication between sensors and the cloud. We propose an online, communication-efficient method to detect such changes. The procedure works by performing likelihood ratio tests at each time point, and two thresholds are chosen to filter unimportant test statistics and make decisions based on the aggregated test statistics respectively. We provide asymptotic theory concerning consistency and the asymptotic distribution if there are no changes. Simulation results suggest that our method can achieve similar performance to the idealised setting, where we have no constraints on communication between sensors, but substantially reduce the transmission costs.

A constant-per-iteration likelihood ratio test for online changepoint detection for exponential family models

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 introduced for detecting changes in mean in Gaussian data that decreases the per-iteration cost to O(logT)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(\\log T)$$\\end{document}. This is possible by using pruning ideas, which reduce the set of changepoint locations that need to be considered at time T to approximately logT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\log T$$\\end{document}. We show that if one wishes to perform the likelihood ratio test for a different one-parameter exponential family model, then exactly the same pruning rule can be used, and again one need only consider approximately logT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\log T$$\\end{document} locations at iteration T. Furthermore, we show how we can adaptively perform the maximisation step of the algorithm so that we need only maximise the test statistic over a small subset of these possible locations. Empirical results show that the resulting online algorithm, which can detect changes under a wide range of models, has a constant-per-iteration cost on average.

Assessing effects of pandemic-related policies on individual public transit travel patterns: A Bayesian online changepoint detection based framework

During a pandemic or natural disaster, people may alter transit usage behavior due to perception of changes in the environment. To effectively respond to these crises, it is important for governments and public transit agencies to understand when these changes occurred and how they were affected by relevant policies and responsive strategies. In this study, we develop a methodological framework based on Bayesian online changepoint detection (BOCD) to identify the occurrence time, direction, and persistency of changes in individual-level transit usage. We demonstrate the effectiveness of this framework in informing government decision-making in the context of COVID-19. Using Jeju Island, South Korea as a case study, we apply the framework over a nearly two-year smart card dataset collected from the beginning of 2019 till nine months into the pandemic. By focusing on frequent transit users, we detect when these users significantly changed their transit usage frequency during the pandemic and identify several types of users who experienced different behavior change patterns. Besides demonstrating the great heterogeneity in individual-level behavior changes, we perform a regression analysis to further understand how these changes were affected by key government policies (e.g., Risk alert, Social distancing, Public transit policy, and Eased social distancing). Our results suggest that only certain sets of policies appear to have significant effects. In particular, introducing Risk alert would cause a 277% to 317% increase in the number of users who reduced transit usage frequency. Policies that eased social distancing, though, would cause a 134% to 155% increase in the number of users with travel frequency increase. The proposed BOCD framework enables a scalable solution to identifying and understanding changes of individual transit behavior. The methodology and findings are beneficial for developing targeted policies and interventions to facilitate daily travel and public transit operations during public health crises.

Gamma-Ray Burst Detection with Poisson-FOCuS and Other Trigger Algorithms

We describe how a novel online change-point detection algorithm, called Poisson-FOCuS, can be used to optimally detect gamma-ray bursts within the computational constraints imposed by miniaturized satellites such as the upcoming HERMES-Pathfinder constellation. Poisson-FOCuS enables testing for gamma-ray burst onset at all intervals in a count time series, across all timescales and offsets, in real time and at a fraction of the computational cost of conventional strategies. We validate an implementation with automatic background assessment through exponential smoothing, using archival data from Fermi-GBM. Through simulations of lightcurves modeled after real short and long gamma-ray bursts, we demonstrate that the same implementation has higher detection power than algorithms designed to emulate the logic of Fermi-GBM and Compton-BATSE, reaching the performance of a brute-force benchmark with oracle information on the true background rate, when not hindered by automatic background assessment. Finally, using simulated data with different lengths and means, we show that Poisson-FOCuS can analyze data twice as fast as a similarly implemented benchmark emulator for the historic Fermi-GBM on-board trigger algorithms.

A Log-Linear Nonparametric Online Changepoint Detection Algorithm Based on Functional Pruning

A Log-Linear Nonparametric Online Changepoint Detection Algorithm Based on Functional Pruning

A note on online change point detection

We investigate sequential change point estimation and detection in univariate nonparametric settings, where a stream of independent observations from sub-Gaussian distributions with a common variance factor and piecewise-constant but otherwise unknown means are collected. We develop a simple CUSUM-based methodology that provably control the probability of false alarms or the average run length while minimizing, in a minimax sense, the detection delay. We allow for all the model parameters to vary in order to capture a broad range of levels of statistical hardness for the problem at hand. We further show how our methodology is applicable to the case in which multiple change points are to be estimated sequentially.

Analysis of time-series monitoring data for early detection of CO2 leakage to shallow subsurface environments

Time-series data of environmental parameters have been gathered in numerous artificial CO2 release experiments performed globally; however, their usability must be re-evaluated because they are generally utilized as auxiliary data, such as model construction or system operation checks. In this study, we propose a process for determining the environmental changepoint (EnCP) using continuous data for the reliable fast detection of unexpected CO2 intrusion into a shallow subsurface. From a controlled CO2 gas release test, continuous data on electrical conductivity, pH, and partial pressure of CO2 (pCO2) in the saturated zone, as well as CO2 concentration in the vadose zone were obtained and analyzed using four methods (simple excess, simple moving average, offline, and online change-point detection) to determine the environmental change-point caused by CO2 appearance. We found that changes caused by CO2 gas injection occurred rapidly, allowing the EnCP detected in the time-series analysis to identify changes more efficiently than the occasional sampling, which was performed at least once a day. It was confirmed that pCO2 had the highest sensitivity among the saturated zone indicators, and the online change-point method was more successful than other methods in determining EnCP effectively. Furthermore, the determined EnCPs at each monitoring point, showing the front boundary of the injected CO2, allowed for the comprehensive understanding of CO2 behavior in both the saturated and vadose zones. This may be used to aid in the design and operation of a real-time CO2 leakage surveillance network system for the early detection of CO2 intrusion into shallow subsurface environments.

RSO proper elements: Concept, methods, and application to maneuver detection

Asteroid families consist of swarms of fragments generated after energetic interasteroidal collisions in the distant past that resulted in the breakup of their progenitors. They are recognized by searching for clusters in the three-dimensional space of “proper elements”; parameters characterizing the asteroid orbits that are very close to fundamental invariants of motion, and thus keep a dynamical record of the initial proximity of the orbits generated by a catastrophic fragmentation event. In the circumterrestrial context, proper elements have recently been applied to the dynamical taxonomy of resident space objects (RSO) for the association of debris from breakup into its “parent” satellite. Here, we clarify, adapt, and extend this fundamental concept to space situational awareness for on-orbit maneuver/anomaly detection. RSO proper elements, being linked to the underlying dynamical structure of orbits, can provide a more robust metric within existing maneuver-detection algorithms, through assessment of the induced changes in these quasi invariants of the motion. We highlight the deeper connection of proper elements to classical frozen orbits in Earth-satellite dynamics and to secular elements in artificial satellite theories based on canonical perturbation theory and showcase several techniques for their numerical computation, including a newly proposed Ptolemy method; applicable to both the asteroid and space-debris domains. We apply a Bayesian online changepoint-detection (BOCD) algorithm in both mean- and proper-element space and compare their performances for orbit-maneuver detection.

Ultimate Limits for Quickest Quantum Change-Point Detection.

Detecting abrupt changes in data streams is crucial because they are often triggered by events that have important consequences if left unattended. Quickest change-point detection has become a vital sequential analysis primitive that aims at designing procedures that minimize the expected detection delay of a change subject to a bounded expected false alarm time. We put forward the quantum counterpart of this fundamental primitive on streams of quantum data. We give a lower bound on the mean minimum delay when the expected time of a false alarm is asymptotically large, under the most general quantum detection strategy, which is given by a sequence of adaptive collective (potentially weak) measurements on the growing string of quantum data. In addition, we give particular strategies based on repeated measurements on independent blocks of samples that asymptotically attain the lower bound and thereby establish the ultimate quantum limit for quickest change-point detection. Finally, we discuss online change-point detection in quantum channels.

Performance evaluation of multivariate statistical techniques using edge-enabled optimisation for change detection in activity monitoring

The monitoring of human activities using simple body worn sensors is an important and emerging area of research in machine learning. The sensors capture a large amount of data in a short period of Time in a relatively un-obtrusive manner. The sensor data might have different transitions to be used for deification of different user activities. Therefore, change point detection can be used to classify the transition from one underlying distribution to another. The automatic and accurate change point detection is not only used for different events, however, can also be used for generating real world datasets and responding to changes in patient vital signs in critical situation. Moreover, the huge amount of data can use the current state-of-the-art cloud and edge computing platforms to process the change detection locally and more efficiently. In this paper, we used multivariate exponentially weighted moving Average (MEWMA) for online change point detection. Additionally, genetic algorithm (GA) and particle swarm optimization (PSO) is used to automatically identify an optimal parameter set by maximizing the F-measure. The optimisation approach is implemented over an edge cloud platform so that the data can be processed locally and more accurately. Furthermore, we evaluate our approach against multivariate cumulative sum (MCUSUM) from state-of the-art in terms of different metric measures such as accuracy, precision, sensitivity, G-means and F-measure. Results have been evaluated based on real data set collected using accelerometer for a set of 9 distinct activities performed by 10 users for total period of 35 minutes with achieving high accuracy from 99.3% to 99.9% and F-measure up to 62.94%.

Inference in High-dimensional Online Changepoint Detection

We introduce and study two new inferential challenges associated with the sequential detection of change in a high-dimensional mean vector. First, we seek a confidence interval for the changepoint, and second, we estimate the set of indices of coordinates in which the mean changes. We propose an online algorithm that produces an interval with guaranteed nominal coverage, and whose length is, with high probability, of the same order as the average detection delay, up to a logarithmic factor. The corresponding support estimate enjoys control of both false negatives and false positives. Simulations confirm the effectiveness of our methodology, and we also illustrate its applicability on the US excess deaths data from 2017–2020. The supplementary material, which contains the proofs of our theoretical results, is available online.

Real-time changepoint detection in a nonlinear expectile model

An online changepoint detection procedure based on conditional expectiles is introduced. The key contribution is threefold: nonlinearity of the underlying model improves the overall flexibility while a parametric form of the unknown regression function preserves a simple and straightforward interpretation; The conditional expectiles, well-known in econometrics for being the only coherent and elicitable risk measure, introduce additional robustness—especially with respect to asymmetric error distributions common in various types of data; The proposed statistical test is proved to be consistent and the distribution under the null hypothesis does not depend on the functional form of the underlying model nor the unknown parameters. Empirical properties of the proposed real-time changepoint detection test are investigated in a simulation study and a practical applicability is illustrated using the Covid-19 prevalence data from Prague.

Online change-point detection for a transient change

We consider a popular online change-point problem of detecting a transient change in distributions of i.i.d. random variables. For this change-point problem, several change-point procedures are formulated and some advanced results for a particular procedure are surveyed. Some new approximations for the average run length to false alarm are offered and the power of these procedures for detecting a transient change in mean of a sequence of normal random variables is compared.

Predictive ratio CUSUM (PRC): A Bayesian approach in online change point detection of short runs

The online quality monitoring of a process with low volume data is a very challenging task and the attention is most often placed in detecting when some of the underline (unknown) process parameter(s) experience a persistent shift. Self-starting methods, both in the frequentist and the Bayesian domain aim to offer a solution. Adopting the latter perspective, we propose a general closed-form Bayesian scheme, where the testing procedure is built on a memory-based control chart that relies on the cumulative ratios of sequentially updated predictive distributions. The theoretic framework can accommodate any likelihood from the regular exponential family and the use of conjugate analysis allows closed form modeling. Power priors will offer the axiomatic framework to incorporate into the model different sources of information, when available. A simulation study evaluates the performance against competitors and examines aspects of prior sensitivity. Technical details and algorithms are provided as supplementary material.

Online change-point detection: a weighted sum approach with constraint and application to dynamic network observation

This article considers the change detection problem for one-dimensional observation sequences and dynamic network observation sequences. Since for network data, especially for large-scale network data, it is unrealistic to obtain the underlying distribution or underlying probabilistic structure. In this view, we consider a weighted sum approach with constraint for change detection. The purpose of the constraint is to highlight the changes, thus the method proposed has better performance for small shift. Meanwhile, different metrics for network data are suitable for different types of changes, the detection ability of L 1-norm is better for dense change, and the detection ability of max-norm is better for sparse change. A parallel multi-chart is proposed as a guidance for improving the performance of change detection for different types of changes. Furthermore, the theoretical results are illustrated numerically on one-dimensional observation sequences and dynamic network observation sequences.

Online Change Point Detection in Application With Transition-Aware Activity Recognition

Transition-aware activity recognition is an inherent component of online health monitoring and ambient assisted living. An explosion of technology breakthroughs in wireless sensor networks, wearable computing, and mobile computing has facilitated this. However, real time, dynamic activity recognition is still challenging in practice. As reported in the existing literature, machine learning techniques are successfully used on the presegmented data to deliver transition-aware activity recognition systems. However, these strategies are frequently ineffective when used in a near-real-time context. This article presents an online change point detection (OCPD) strategy to segment the continuous multivariate time-series smartphone sensor data and its application in a transition-aware activity recognition framework. The proposed OCPD strategy is based on the hypothesis-and-verification principle. After the online data stream segmentation using the proposed OCPD strategy, feature engineering is performed to retain the essential features. Then, synthetic minority oversampling technique (SMOTE) is applied to balance the dataset. Finally, practical experiments are carried out to verify the suggested frameworks’ efficiency and reliability. The results reveal that the proposed OCPD strategy with ensemble classifier achieves a greater recognition rate (F-Measure: 99.80%) compared to methods stated in the literature.

A Data-Driven Framework for FDI Attack Detection and Mitigation in DC Microgrids

This paper proposes a Data-Driven (DD) framework for the real-time monitoring, detection, and mitigation of False Data Injection (FDI) attacks in DC Microgrids (DCMGs). A supervised algorithm is adopted in this framework to continuously estimate the output voltage and current for all Distributed Generators (DGs) with acceptable accuracy. Accordingly, among the various evaluated supervised DD algorithms, Adaptive Neuro-Fuzzy Inference Systems (ANFISs) are utilized because of their low computational burden, efficiency in operation, and simplicity in design and implementation in a distributed control system. The proposed framework is based on the residual analysis of the generated error signal between the estimated and actual sensed signals. The proposed framework detects and mitigates the cyber-attack depending on trends in generated error signals. Moreover, by applying Online Change Point Detection (OCPD), the need for a static user-defined threshold for the residual analysis of the generated error signal is dispelled. Finally, the proposed method is validated in a MATLAB/Simulink testbed, considering the resilience, effectiveness, accuracy, and robustness of multiple case study scenarios.

Quantitative Evaluation of Aerosol Generation During In-Office Flexible Laryngoscopy

Despite growing scientific knowledge and research, it is still unknown if office flexible laryngoscopy (FL) is aerosol generating and thereby potentially increases the risk of SARS-CoV-2 transmission. The limited literature that exists is conflicting, precluding formal conclusions. To determine whether FL is aerosol generating. This prospective cohort study included 134 patients seen in the otolaryngology clinic at a single tertiary care academic institution between February and May 2021. Two optical particle sizer instruments were used, quantifying particles ranging from 0.02 μm to 5 μm. Measurements were taken every 30 seconds, with sample periods of 15 seconds throughout the patient encounter. Instruments were located 12 inches from the patient's nares. Timing of events was recorded, including the start and end of physical examination, topical spray administration, start and end of laryngoscopy, and other potential aerosol-generating events (eg, coughing, sneezing). Data analysis was performed from February to May 2021. Office examination and office FL. Bayesian online change point detection (OCPD) algorithm was used to detect significant change points (CPs) in this time-series data. The primary outcome was significant CP after FL compared with baseline physiologic variations, such as breathing and phonation. Data were collected from 134 patients between February and May 2021. Ninety-one encounters involved FL. Of this group, 51 patients (56%) wore no mask over their mouth during FL. There was no statistically significant CP in either visits involving FL or visits where FL was not performed. Use of nasal spray did not result in CP in aerosol levels. Overall, neither the number of people present in the examination room, masks over patients' mouth, the duration of the visit, nor the duration of FL were associated with mean aerosol counts, regardless of the exposure. For larger aerosol sizes (≥1 μm), however, rooms with higher air exchange rates had significantly higher reductions in mean aerosol counts for visits involving FL. The findings of this cohort study support that FL, including topical spray administration, is not a significant aerosol-generating procedure. The Bayesian OCPD model has a promising application for future aerosol studies in otolaryngology.

Online change-point detection with kernels

Change-points in time series data are usually defined as the time instants at which changes in their properties occur. Detecting change-points is critical in a number of applications as diverse as detecting credit card and insurance frauds, or intrusions into networks. Recently the authors introduced an online kernel-based change-point detection method built upon direct estimation of the density ratio on consecutive time intervals. This paper further investigates this algorithm, making improvements and analyzing its behavior in the mean and mean square sense, in the absence and presence of a change point. These theoretical analyses are validated with Monte Carlo simulations. The detection performance of the algorithm is illustrated through experiments on real-world data and compared to state of the art methodologies.