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1. Scalable multiple changepoint detection for functional data sequences

   https://doi.org/10.1002/env.2710  

   Harris, Trevor ; Li, Bo ; Tucker, J. Derek ( November 2021 , Environmetrics)

   We propose the multiple changepoint isolation (MCI) method for detecting multiple changes in the mean and covariance of a functional process. We first introduce a pair of projections to represent the variability “between” and “within” the functional observations. We then present an augmented fused lasso procedure to split the projections into multiple regions robustly. These regions act to isolate each changepoint away from the others so that the powerful univariate CUSUM statistic can be applied region‐wise to identify the changepoints. Simulations show that our method accurately detects the number and locations of changepoints under many different scenarios. These include light and heavy tailed data, data with symmetric and skewed distributions, sparsely and densely sampled changepoints, and mean and covariance changes. We show that our method outperforms a recent multiple functional changepoint detector and several univariate changepoint detectors applied to our proposed projections. We also show that MCI is more robust than existing approaches and scales linearly with sample size. Finally, we demonstrate our method on a large time series of water vapor mixing ratio profiles from atmospheric emitted radiance interferometer measurements.

    

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2. Improved Time-Localization of Power System Forced Oscillations Using Changepoint Detection

   https://doi.org/10.1109/PESGM48719.2022.9916848  

   Dosiek, Luke ; Hosur, Sanjay ( July 2022 , 2022 IEEE Power & Energy Society General Meeting (PESGM))

   This paper explores the use of changepoint detection (CPD) for an improved time-localization of forced oscillations (FOs) in measured power system data. In order for the autoregressive moving average plus sinusoids (ARMA+S) class of electromechanical mode meters to successfully estimate modal frequency and damping from data that contains a FO, accurate estimates of where the FO exists in time series are needed. Compared to the existing correlation-based method, the proposed CPD method is based on upon a maximum likelihood estimator (MLE) for the detection of an unknown number changes in signal mean to unknown levels at unknown times. Using the pruned exact linear time (PELT) dynamic programming algorithm along with a novel refinement technique, the proposed approach is shown to provide a dramatic improvement in FO start/stop time estimation accuracy while being robust to intermittent FOs. These findings were supported though simulations with the minniWECC model. 

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3. An Iterative Algorithm for Accurate Estimation of Power System Forced Oscillation Parameters

   https://doi.org/10.1109/PESGM52003.2023.10253231  

   Dosiek, Luke ; Hosur, Sanjay ( July 2023 , 2023 IEEE Power & Energy Society General Meeting (PESGM))

   This paper proposes an iterative method of estimating power system forced oscillation (FO) amplitude, frequency, phase, and start/stop times from measured data. It combines three algorithms with favorable asymptotic statistical properties: a periodogram-based iterative frequency estimator, a Discrete-Time Fourier Transform (DTFT)-based method of estimating amplitude and phase, and a changepoint detection (CPD) method for estimating the FO start and stop samples. Each of these have been shown in the literature to be approximate maximum likelihood estimators (MLE), meaning that for large enough sample size or signal-to-noise ratio (SNR), they can be unbiased and reach the Cramer-Rao Lower Bound in variance. The proposed method is shown through Monte Carlo simulations of a low-order model of the Western Electricity Coordinating Council (WECC) power system to achieve statistical efficiency for low SNR values. The proposed method is validated with data measured from the January 11, 2019 US Eastern Interconnection (EI) FO event. It is shown to accurately extract the FO parameters and remove electromechanical mode meter bias, even with a time-varying FO amplitude. 

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4. A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening

   Lin, Kevin ; Sharpnack, James L ; Rinaldo, Alessandro ; Tibshirani, Ryan J ( December 2017 , Advances in neural information processing systems)

   In the 1-dimensional multiple changepoint detection problem, we derive a new fast error rate for the fused lasso estimator, under the assumption that the mean vector has a sparse number of changepoints. This rate is seen to be suboptimal (compared to the minimax rate) by only a factor of loglogn. Our proof technique is centered around a novel construction that we call a lower interpolant. We extend our results to misspecified models and exponential family distributions. We also describe the implications of our error analysis for the approximate screening of changepoints. 

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5. Reassessing the Constancy of End-to-End Internet Latency

   Davisson, Lily ; Jakovleski, Joakim ; Ngo, Nhiem ; Pham, Chau ; Sommers, Joel ( September 2021 , IFIP Network Traffic Measurement and Analysis Conference)

   A paper by Zhang et al. in 2001, “On the Constancy of Internet Path Properties” [1] examined the constancy of end- to-end packet loss, latency, and throughput using a modest set of hosts deployed in the Internet. In the time since that work, the Internet has changed dramatically, including the flattening of the autonomous system hierarchy and increased deployment of IPv6, among other developments. In this paper, we investigate the constancy of end-to-end Internet latency, revisiting findings of the earlier study. We use latency measurements from RIPE Atlas, choosing a set of 124 anchors with broad geographic distribution and drawn from 112 distinct autonomous systems. The earlier work of Zhang et al. relies on changepoint detection methods to identify mathematically constant time periods. We reimplement the two methods described in that earlier work and use them on the RIPE Atlas latency measurements. We also use a recently- published method (HMM-HDP) that has direct support in a RIPE Atlas API. Comparing the three changepoint detection methods, we find that the two methods used in the earlier work may miss many changepoints caused by common level-shift events. Overall, we find that the recently proposed HMM-HDP method performs substantially better. Moreover, we find that delay spikes—as defined by the earlier work—are an order of magnitude less prevalent than 20 years ago. We also find that maximum change- free regions (CFRs) along paths that we observe in today’s Internet are substantially longer than what was observed in 2001, regardless of the changepoint detection method used. In particular, the 50th percentile maximum CFR was on the order of 30 minutes in the earlier study, but our analysis reveals it to be on the order of 3 days or longer. Moreover, we find that CFR durations appear to have steadily increased over the past 5 years. 

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