Quickest Detection of Parameter Changes in Stochastic Regression: Nonparametric CUSUM

Konev, Victor; Vorobeychikov, Sergey E.

doi:10.1109/tit.2017.2673825

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…For example, the authors in [20] provide a CUSUM stopping rule with application in computer vision problems. A CUSUM approach for CP detection on observations with an unknown distribution before and after a change, has been recently developed in [21]. Furthermore, an algorithm based on the Shiryaev-Roberts procedure was proposed in [22], to detect anomalies in computer network traffic.…”

Section: B Parametric and Non Parametric Cp Detection Algorithmsmentioning

confidence: 99%

Real-Time Algorithms for the Detection of Changes in the Variance of Video Content Popularity

2020

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As video content is responsible for more than 70% of the global IP traffic, related resource allocation approaches, e.g., using content caching, become increasingly important. In this context, to avoid under-provisioning, it is important to rapidly detect and respond to changes in content popularity dynamics, including volatility, i.e., changes in the second order moment of the underlying process. In this paper, we focus on the early identification of changes in the variance of video content popularity, which we address as a statistical change point (CP) detection problem. Unlike changes in the mean that can be well captured by non-parametric statistical approaches, to address this more demanding problem, we construct a hypothesis test that uses in the test statistic both parametric and non-parametric approaches. In the context of parametric models, we consider linear, in the form of autoregressive moving average (ARMA), and, nonlinear, in the form of generalized autoregressive conditional heteroskedasticity (GARCH) processes. We propose an integrated algorithm that combines off-line and on-line CP schemes, with the off-line scheme used as a training (learning) phase. The algorithm is first assessed over synthetic data; our analysis demonstrates that non parametric and GARCH model based approaches can better generalize and are better suited for content views time series with unknown statistics. Finally, the non-parametric and the GARCH based variations of our proposed integrated algorithm are applied on real YouTube video content views time series, to illustrate the performance of the proposed approach of volatility change detection. INDEX TERMS Content popularity dynamics detection, change point analysis, variance change detection, volatility detection.

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Section: B Parametric and Non Parametric Cp Detection Algorithmsmentioning

confidence: 99%

Real-Time Algorithms for the Detection of Changes in the Variance of Video Content Popularity

2020

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“…29 A nonparameter CUSUM is designed for quickest detection of parameter changes in stochastic regression. 30 The CUSUM is more appropriate in the target tracking problem discussed in the article, because the measurement vector of radar comes according to the time sequence. Therefore, we use the CUSUM to detect the measurement mismatch in this article.…”

Section: A Practical Adaptive Nonlinear Tracking Algorithm With the Rmentioning

confidence: 99%

A practical adaptive nonlinear tracking algorithm with range rate measurement

Yang

Hongqiang

Zhou

et al. 2018

International Journal of Distributed Sensor Networks

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It is difficult to answer the problem whether the range rate measurement should be adopted to track a target in a tracking scenario. A practical adaptive nonlinear tracking algorithm with the range rate measurement is proposed, which avoids this problem and achieves good accuracy of target state estimation. First, three popular nonlinear filtering algorithms only with the position measurement are surveyed. Second, three popular nonlinear filtering algorithms with the position and range rate measurements are surveyed. Then, a novel tracking algorithm with range rate measurement is proposed based on the cumulative sum detector and the above two kinds of nonlinear algorithms. The results of simulation experiment demonstrate that the range rate measurement could reduce accuracy of the target state estimation in mismatch tracking scenarios. The results of simulation experiment also verify that the performance of proposed algorithm is better than the current state and the art interacting multiple-model algorithm and can well follow the state estimation output of the measurement equation matching the tracking scenario.

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“…Given hundreds of thousands of time series with a wide degree of variance profiles, it is difficult to justify consistent assumptions about a stationary error distribution required for a parametric model. This consequently led us to non-parametric methods such as the commonly used CUSUM algorithm [10,14,16,22].…”

Section: Introductionmentioning

confidence: 99%

A Real-time Framework for Detecting Efficiency Regressions in a Globally Distributed Codebase

Valdez-Vivas

Gocmen

Korotkov

et al. 2018

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Multiple teams at Facebook are tasked with monitoring compute and memory utilization metrics that are important for managing the efficiency of the codebase. An efficiency regression is characterized by instances where the CPU utilization or query per second (QPS) patterns of a function or endpoint experience an unexpected increase over its prior baseline. If the code changes responsible for these regressions get propagated to Facebook's fleet of web servers, the impact of the inefficient code will get compounded over billions of executions per day, carrying potential ramifications to Facebook's scaling efforts and the quality of the user experience. With a codebase ingesting in excess of 1,000 diffs across multiple pushes per day, it is important to have a real-time solution for detecting regressions that is not only scalable and high in recall, but also highly precise in order to avoid overrunning the remediation queue with thousands of false positives. This paper describes the end-to-end regression detection system designed and used at Facebook. The main detection algorithm is based on sequential statistics supplemented by signal processing transformations, and the performance of the algorithm was assessed with a mixture of online and offline tests across different use cases. We compare the performance of our algorithm against a simple benchmark as well as a commercial anomaly detection software solution. CCS CONCEPTS • Computer systems organization → Maintainability and maintenance; Real-time systems; • Software and its engineering → Operational analysis; • Information systems → Decision support systems; • Computing methodologies → Anomaly detection; • Mathematics of computing → Time series analysis;

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Quickest Detection of Parameter Changes in Stochastic Regression: Nonparametric CUSUM

Cited by 8 publications

References 21 publications

Real-Time Algorithms for the Detection of Changes in the Variance of Video Content Popularity

Real-Time Algorithms for the Detection of Changes in the Variance of Video Content Popularity

A practical adaptive nonlinear tracking algorithm with range rate measurement

A Real-time Framework for Detecting Efficiency Regressions in a Globally Distributed Codebase

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