More Like this

1. Active multi-fidelity Bayesian online changepoint detection

   Gundersen, Gregory W.; Cai, Diana; Zhou, Chuteng; Engelhardt, Barbara E.; Adams, Ryan P. (January 2021, Uncertainty in artificial intelligence)

   null (Ed.)

   Online algorithms for detecting changepoints, or abrupt shifts in the behavior of a time series, are often deployed with limited resources, e.g., to edge computing settings such as mobile phones or industrial sensors. In these scenarios it may be beneficial to trade the cost of collecting an environmental measurement against the quality or "fidelity" of this measurement and how the measurement affects changepoint estimation. For instance, one might decide between inertial measurements or GPS to determine changepoints for motion. A Bayesian approach to changepoint detection is particularly appealing because we can represent our posterior uncertainty about changepoints and make active, cost-sensitive decisions about data fidelity to reduce this posterior uncertainty. Moreover, the total cost could be dramatically lowered through active fidelity switching, while remaining robust to changes in data distribution. We propose a multi-fidelity approach that makes cost-sensitive decisions about which data fidelity to collect based on maximizing information gain with respect to changepoints. We evaluate this framework on synthetic, video, and audio data and show that this information-based approach results in accurate predictions while reducing total cost. 

   more » « less
2. 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. 

   more » « less
3. Homogeneity tests of covariance matrices with high-dimensional longitudinal data

   https://doi.org/10.1093/biomet/asz011  

   Zhong, Ping-Shou; Li, Runze; Santo, Shawn (May 2019, Biometrika)

   Summary This paper deals with the detection and identification of changepoints among covariances of high-dimensional longitudinal data, where the number of features is greater than both the sample size and the number of repeated measurements. The proposed methods are applicable under general temporal-spatial dependence. A new test statistic is introduced for changepoint detection, and its asymptotic distribution is established. If a changepoint is detected, an estimate of the location is provided. The rate of convergence of the estimator is shown to depend on the data dimension, sample size, and signal-to-noise ratio. Binary segmentation is used to estimate the locations of possibly multiple changepoints, and the corresponding estimator is shown to be consistent under mild conditions. Simulation studies provide the empirical size and power of the proposed test and the accuracy of the changepoint estimator. An application to a time-course microarray dataset identifies gene sets with significant gene interaction changes over time. 

   more » « less
4. An Exploratory Study of Project Activity Changepoints in Open Source Software Evolution

   https://doi.org/10.1109/MSR52588.2021.00088  

   Walden, James; Burgin, Noah; Kaur, Kuljit (May 2021, 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR))

   null (Ed.)

   To explore the prevalence of abrupt changes (changepoints) in open source project activity, we assembled a dataset of 8,919 projects from the World of Code. Projects were selected based on age, number of commits, and number of authors. Using the nonparametric PELT algorithm, we identified changepoints in project activity time series, finding that more than 90% of projects had between one and six changepoints. Increases and decreases in project activity occurred with roughly equal frequency. While most changes are relatively small, on the order of a few authors or few dozen commits per month, there were long tails of much larger project activity changes. In future work, we plan to focus on larger changes to search for common open source lifecycle patterns as well as common responses to external events. 

   more » « less
5. Autocovariance estimation in the presence of changepoints

   https://doi.org/10.1007/s42952-022-00173-5  

   Gallagher, Colin; Killick, Rebecca; Lund, Robert; Shi, Xueheng (June 2022, Journal of the Korean Statistical Society)

   This article studies estimation of a stationary autocovariance structure in the presence of an unknown number of mean shifts. Here, a Yule–Walker moment estimator for the autoregressive parameters in a dependent time series contaminated by mean shift changepoints is proposed and studied. The estimator is based on first order differences of the series and is proven consistent and asymptotically normal when the number of changepoints m and the series length N satisfy 𝑚/𝑁→0 as 𝑁→∞. 

   more » « less