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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)

   Abstract 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. 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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3. 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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4. Covariate Software Defect Discovery Models to Explicitly Characterize Changepoints

   https://doi.org/10.1142/S0218539325500445  

   Steakelum, Joshua; Nagaraju, Vidhyashree; Shi, Ying; Fiondella, Lance (December 2025, International Journal of Reliability, Quality and Safety Engineering)

   Large-scale software exhibits periods of increased defect discovery when blocks of less thoroughly tested code are introduced into an existing codebase. For example, the mission systems schedule of software intensive government acquisition programs includes multiple overlapping software blocks associated with various capabilities. Software reliability researchers have proposed changepoint models to characterize periods of increased defect discovery. However, these models attempt to identify the location of these changepoints after testing has been performed, which is counter-intuitive because conscious decisions such as testing new functionality drive software changepoints. Existing changepoint models are therefore difficult to employ in a predictive manner. To overcome this limitation, this paper proposes a covariate software defect discovery model capable of explaining changepoints in terms of common software testing activities and metrics such as software size estimation, code coverage, and defect density. The proposed and past changepoint models are compared with respect to their predictive accuracy and computational efficiency. Our results indicate that the proposed approach is more computationally efficient and enables accurate prediction of the time needed to achieve a desired defect discovery intensity or mean time to failure despite the occurrence of changepoints during software testing. 

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5. 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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