Journal of Time Series Analysis最新文献

{"title":"Simultaneous Detection of Structural Breaks and Outliers in Time Series","authors":"Richard A. Davis,&nbsp;Thomas C. M. Lee,&nbsp;Gabriel A. Rodriguez-Yam","doi":"10.1111/jtsa.70010","DOIUrl":"https://doi.org/10.1111/jtsa.70010","url":null,"abstract":"<div>\u0000 \u0000 <p>This article considers the problem of modeling a class of nonstationary time series using piecewise autoregressive (AR) processes in the presence of outliers. The number and locations of the piecewise AR segments, as well as the orders of the respective AR processes, are assumed to be unknown. In addition, each piece may contain an unknown number of innovational and/or additive outliers. The minimum description length (MDL) principle is applied to compare various segmented AR fits to the data. The goal is to find the “best” combination of the number of segments, the lengths of the segments, the orders of the piecewise AR processes, and the number and type of outliers. Such a “best” combination is implicitly defined as the optimizer of an MDL criterion. Since the optimization is carried over a large number of configurations of segments and positions of outliers, a genetic algorithm is used to find optimal or near-optimal solutions. Numerical results from simulation experiments and real data analyses show that the procedure enjoys excellent empirical properties.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 3","pages":"485-505"},"PeriodicalIF":1.0,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional Sieve Bootstrap for the Partial Sum Process With an Application to Change-Point Detection","authors":"Efstathios Paparoditis,&nbsp;Lea Wegner,&nbsp;Martin Wendler","doi":"10.1111/jtsa.12852","DOIUrl":"https://doi.org/10.1111/jtsa.12852","url":null,"abstract":"<p>This article applies the functional sieve bootstrap (FSB) to estimate the distribution of the partial sum process for time series stemming from a weakly stationary functional process. Consistency of the FSB procedure under weak assumptions on the underlying functional process is established. This result allows for the application of the FSB procedure to testing for a change-point in the mean of a functional time series using the CUSUM-statistic. We show that the FSB asymptotically correctly estimates critical values of the CUSUM-based test under the null-hypothesis. Consistency of the FSB-based test under local alternatives is also proven. The finite-sample performance of the procedure is studied via simulations.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 3","pages":"651-659"},"PeriodicalIF":1.0,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12852","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147684075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Data Segmentation in Time Series: Structural Breaks and Real-Time Monitoring","authors":"Alexander Aue,&nbsp;Claudia Kirch","doi":"10.1111/jtsa.70055","DOIUrl":"https://doi.org/10.1111/jtsa.70055","url":null,"abstract":"&lt;p&gt;In his seminal 1954 Biometrika paper (Page &lt;span&gt;1954&lt;/span&gt;), E. S. Page introduced the cumulative sum (CUSUM) control chart for change-point monitoring, a foundational idea that continues to underpin many modern approaches to time-series segmentation. This special issue highlights the breadth of recent advances in the field, spanning online and offline structural break detection, methodologies for high-dimensional and complex data, and techniques for estimating multiple change points.&lt;/p&gt;&lt;p&gt;Multiple change-point estimation, often referred to as data segmentation, seeks to partition a time series into stationary regimes. Because the number of segments is unknown a priori, this task poses additional statistical and computational challenges beyond testing for a single structural break. Addressing these challenges, Barigozzi et al. (&lt;span&gt;2026&lt;/span&gt;) develop a methodology for multiple change-point detection in large factor models, linking segmentation with structural change in high-dimensional time series. Such models are central in econometrics, where a small number of latent factors drive many observed variables; their approach accommodates changes in factor loadings, including the emergence or disappearance of factors. While Barigozzi et al. (&lt;span&gt;2026&lt;/span&gt;) employs moving-sum procedures, Lund et al. (&lt;span&gt;2026&lt;/span&gt;) adopts a penalized likelihood framework that enables detection of multiple changes in non-Gaussian settings, including count time series constructed via suitable transformations of Gaussian processes, thus facilitating likelihood-based inference. In a different direction, Davis et al. (&lt;span&gt;2026&lt;/span&gt;) apply the minimum description length principle to jointly estimate change points and outliers in piecewise linear autoregressive models, allowing for both innovation and additive outliers, two distinct but practically intertwined forms of anomalies. Casini and Perron (&lt;span&gt;2026&lt;/span&gt;) revisit the classical linear regression model under a continuous-record asymptotic framework, where discrete observations arise from underlying continuous-time Itô semimartingales, reflecting a fundamentally different modeling paradigm. Extensions beyond linear models introduce further challenges, e.g., for count data. Whereas Lund et al. (&lt;span&gt;2026&lt;/span&gt;) relies on Gaussian transformations, Hudecová and Hušková (&lt;span&gt;2026&lt;/span&gt;) builds upon a generalized Poisson autoregressive model with exogenous components introduced by Aknouche and Francq (&lt;span&gt;2021&lt;/span&gt;) to propose new change-point tests for count time series. They establish the corresponding limit theory and obtain empirical performance results. Finally, for continuously observed autoregressive models potentially including exogenous covariates, Kirch and Schwaar (&lt;span&gt;2026&lt;/span&gt;) develop change-point tests based on neural network approximations of nonlinear regression functions, illustrating the growing integration of machine learning techniques into structural break analysis","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 3","pages":"447-449"},"PeriodicalIF":1.0,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Robust Topological Framework for Detecting Regime Changes in Multi-Trial Experiments With Application to Predictive Maintenance","authors":"Anass El-Yaagoubi,&nbsp;Jean-Marc Freyermuth,&nbsp;Hernando Ombao","doi":"10.1111/jtsa.70032","DOIUrl":"https://doi.org/10.1111/jtsa.70032","url":null,"abstract":"<div>\u0000 \u0000 <p>We present a general and adaptable framework for detecting regime changes in complex, non-stationary data across multi-trial experiments. While traditional change point detection methods focus on identifying abrupt changes within a single time series (single trial), our approach identifies changes that occur across trials, accommodating variations due to experimental inconsistencies, such as differing event timings or durations. By utilizing diverse metrics, including topological analysis of time-frequency characteristics in the spectrum and spectrograms, our method provides a robust framework for detecting cross-trial changes. This flexibility allows it to address a range of scenarios with varying statistical assumptions, including different levels of stationarity within and across trials. We validate our approach through simulations using time-varying autoregressive processes exhibiting various regime changes. Our results highlight the method's effectiveness in detecting cross-trial changes under varied conditions. Furthermore, we showcase its potential for practical applications by analyzing vibration signals from the NASA bearing dataset. Through time-frequency analysis, our framework accurately identifies bearing failures, demonstrating its strong capability for early fault detection in predictive maintenance of mechanical systems.</p>\u0000 </div>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 3","pages":"579-596"},"PeriodicalIF":1.0,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"mixFOCuS: A Communication-Efficient Online Changepoint Detection Method in Distributed System for Mixed-Type Data","authors":"Ziyang Yang,&nbsp;Idris A. Eckley,&nbsp;Paul Fearnhead","doi":"10.1111/jtsa.12834","DOIUrl":"https://doi.org/10.1111/jtsa.12834","url":null,"abstract":"<p>With the advent of the Internet of Things, it is increasingly common to have large networks of sensors, where each sensor may collect different types of data, has limited local computing resources and the ability to transmit data to a central cloud. Detecting events that trigger changes in sensor data properties is a key concern. However, minimizing sensor-to-cloud communication might be necessary due either to privacy constraints or limited battery resources. To detect changes within such a network, we introduce a new method, mixFOCuS, which can detect changes in mixed-type data, where data from different sensors follow different, possibly non-Gaussian, distributions. This methods builds on the FOCuS algorithms, which are recent improvements of the classic approach of Page (1954), extending the approach to streaming data setting for distributed sensor networks. Our method does not require assuming known pre- and post-change parameters, yet is still efficient in both computation and communication, and suitable for detecting changes in real-time. We show the trade-off between reduced transmission frequency and detection power. Simulation results indicate improved power for mixed-type data and better performance than the existing works on Gaussian data.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 3","pages":"701-714"},"PeriodicalIF":1.0,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12834","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gradual Changes in Functional Time Series","authors":"Patrick Bastian,&nbsp;Holger Dette","doi":"10.1111/jtsa.12809","DOIUrl":"https://doi.org/10.1111/jtsa.12809","url":null,"abstract":"<p>We consider the problem of detecting gradual changes in the sequence of mean functions from a not necessarily stationary functional time series. Our approach is based on the maximum deviation (calculated over a given time interval) between a benchmark function and the mean functions at different time points. We speak of a gradual change of size <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 </mrow>\u0000 <annotation>$$ Delta $$</annotation>\u0000 </semantics></math>, if this quantity exceeds a given threshold <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 <mo>&gt;</mo>\u0000 <mn>0</mn>\u0000 </mrow>\u0000 <annotation>$$ Delta &gt;0 $$</annotation>\u0000 </semantics></math>. For example, the benchmark function could represent an average of yearly temperature curves from the pre-industrial time, and we are interested in the question of whether the yearly temperature curves afterwards deviate from the pre-industrial average by more than <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 <mo>=</mo>\u0000 <mn>1</mn>\u0000 <mo>.</mo>\u0000 <mn>5</mn>\u0000 </mrow>\u0000 <annotation>$$ Delta =1.5 $$</annotation>\u0000 </semantics></math> degrees Celsius, where the deviations are measured with respect to the sup-norm. Using Gaussian approximations for high-dimensional data, we develop a test for hypotheses of this type and estimators for the time when a deviation of size larger than <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Δ</mi>\u0000 </mrow>\u0000 <annotation>$$ Delta $$</annotation>\u0000 </semantics></math> appears for the first time. We prove the validity of our approach and illustrate the new methods by a simulation study and a data example, where we analyze yearly temperature curves at different stations in Australia.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 3","pages":"632-650"},"PeriodicalIF":1.0,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12809","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tensor Changepoint Detection and Eigenbootstrap","authors":"Michal Pešta,&nbsp;Barbora Peštová,&nbsp;Martin Romaňák","doi":"10.1111/jtsa.12846","DOIUrl":"https://doi.org/10.1111/jtsa.12846","url":null,"abstract":"<p>Tensor data consisting of multivariate outcomes over the items and across the subjects with longitudinal and cross-sectional dependence are considered. A completely distribution-free and tweaking-parameter-free detection procedure for changepoints at different locations is designed, which does not require training data. A CUSUM type test statistic is employed, and its asymptotic properties are derived for a large number of available individual profiles. The considered test is shown to be consistent. We propose an eigenbootstrap superstructure that overcomes the computational curse of dimensionality without any loss of information, while it preserves all the dependencies within and between the panels. The validity of this new and fast resampling algorithm is proved in this general setting. The empirical properties of the detection technique are investigated through a simulation study. The fully data-driven test is applied to real-world data from EEG and psychometrics.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 3","pages":"557-578"},"PeriodicalIF":1.0,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12846","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiple Changepoint Detection for Non-Gaussian Time Series","authors":"Robert Lund,&nbsp;Thomas J. Fisher,&nbsp;Norou Diawara,&nbsp;Michael Wehner","doi":"10.1111/jtsa.12833","DOIUrl":"https://doi.org/10.1111/jtsa.12833","url":null,"abstract":"<p>This article combines methods from existing techniques to identify multiple changepoints in non-Gaussian autocorrelated time series. A transformation is used to convert a Gaussian series into a non-Gaussian series, enabling penalized likelihood methods to handle non-Gaussian scenarios. When the marginal distribution of the data is continuous, the methods essentially reduce to the change of variables formula for probability densities. When the marginal distribution is count-oriented, Hermite expansions and particle filtering techniques are used to quantify the scenario. Simulations demonstrating the efficacy of the methods are given and two data sets are analyzed: 1) the proportion of home runs hit by Major League Baseball batters from 1920 to 2023 and 2) a six-dimensional series of tropical cyclone counts from the Earth's basins of generation from 1980 to 2023. In the first series, beta marginal distributions are used to describe the proportions; in the second, Poisson marginal distributions seem appropriate.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 3","pages":"465-484"},"PeriodicalIF":1.0,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12833","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tests for Changes in Count Time Series Models With Exogenous Covariates","authors":"Šárka Hudecová,&nbsp;Marie Hušková","doi":"10.1111/jtsa.12830","DOIUrl":"https://doi.org/10.1111/jtsa.12830","url":null,"abstract":"<p>We deal with a parametric change in models for count time series with exogenous covariates specified via the conditional distribution, i.e., with integer generalized autoregressive conditional heteroscedastic models with covariates (INGARCH-X). CUSUM-type methods for the change point detection are proposed, and their asymptotic distributions are derived. The finite-sample behavior of the proposed tests is illustrated through a Monte Carlo simulation study and on a real-time series of daily traffic accident counts.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 3","pages":"526-538"},"PeriodicalIF":1.0,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12830","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial Announcement","authors":"Robert Taylor","doi":"10.1111/jtsa.70042","DOIUrl":"10.1111/jtsa.70042","url":null,"abstract":"<p>I am delighted to welcome Professor Likai Chen (Washington University in St. Louis), Dr. Ilias Chronopoulos (University of Essex), Dr. Adam McCloskey (University of Colorado, Boulder), and Professor Shixuan Wang (University of Reading) to the editorial board of the Journal of Time Series Analysis. All have joined as Associate Editors with effect from 1st January 2026.</p><p>At the same time, I would also like to thank Professor Dennis Kristensen (UCL) for his long and diligent service as an Associate Editor of the Journal of Time Series Analysis. Dennis served in this role from the start of 2013 through to the end of 2025. He leaves us to take up the role of Managing Editor at the <i>Econometrics Journal</i> and we wish him every success in his new role.</p><p>The author declares no conflicts of interest.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"47 2","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.70042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146139634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}