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Research Article

Monitoring of high-dimensional and high-frequency data streams: A nonparametric approach

ORCID Icon, , , &
Received 08 Aug 2023, Accepted 12 May 2024, Published online: 29 May 2024

References

  • Apley, D. W., & Lee, H. C. (2008). Robustness comparison of exponentially weighted moving-average charts on autocorrelated data and on residuals. Journal of Quality Technology, 40(4), 428–447. https://doi.org/10.1080/00224065.2008.11917747
  • Bersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: An overview. Quality and Reliability Engineering International, 23(5), 517–543. https://doi.org/10.1002/qre.829
  • Bodnar, R., Bodnar, T., & Schmid, W. (2023). Sequential monitoring of high-dimensional time series. Scandinavian Journal of Statistics, 50(3), 962–992. https://doi.org/10.1111/sjos.12607
  • Chatterjee, S., & Qiu, P. (2009). Distribution-free cumulative sum control charts using bootstrap-based control limits. The Annals of Applied Statistics, 3(1), 349–369. https://doi.org/10.1214/08-AOAS197
  • Crosier, R. B. (1988). Multivariate generalizations of cumulative sum quality-control schemes. Technometrics, 30(3), 291–303. https://doi.org/10.1080/00401706.1988.10488402
  • Ebrahimi, S., Ranjan, C., & Paynabar, K. (2021). Monitoring and root-cause diagnostics of high-dimensional data streams. Journal of Quality Technology, 54(1), 20–43. https://doi.org/10.1080/00224065.2020.1805377
  • Gandy, A., & Lau, F. D.-H. (2012). Non-restarting cumulative sum charts and control of the false discovery rate. Biometrika, 100(1), 261–268. https://doi.org/10.1093/biomet/ass066
  • Gupta, M., Wadhvani, R., & Rasool, A. (2022). Real-time change-point detection: A deep neural network-based adaptive approach for detecting changes in multivariate time series data. Expert Systems with Applications, 209, 118260. https://doi.org/10.1016/j.eswa.2022.118260
  • Hawkins, D. M. (1987). Self-starting CUSUM charts for location and scale. Journal of the Royal Statistical Society Series D: The Statistician, 36(4), 299–316. https://doi.org/10.2307/2348827
  • Hawkins, D. M. (1991). Multivariate quality control based on regression-adiusted variables. Technometrics, 33(1), 61–75. https://doi.org/10.1080/00401706.1991.10484770
  • Higham, N. J. (1988). Computing a nearest symmetric positive semidefinite matrix. Linear Algebra and Its Applications, 103, 103–118. https://doi.org/10.1016/0024-3795(88)90223-6
  • Khalili, S., & Noorossana, R. (2022). Online monitoring of autocorrelated multivariate linear profiles via multivariate mixed models. Quality Technology & Quantitative Management, 19(3), 319–340. https://doi.org/10.1080/16843703.2021.2015834
  • Lahiri, S. N. (2003). Resampling methods for dependent data. Springer Science & Business Media.
  • Larsen, J. S., Stockmarr, A., Ersbøll, B. K., & Kulahci, M. (2019). Model based level shift detection in autocorrelated data streams using a moving window. arXiv Preprint arXiv: 1503.02531 2. https://doi.org/10.48550/arXiv.1907.05453
  • Li, J. (2019). A two-stage online monitoring procedure for high-dimensional data streams. Journal of Quality Technology, 51(4), 392–406. https://doi.org/10.1080/00224065.2018.1507562
  • Li, J. (2020). Efficient global monitoring statistics for high-dimensional data. Quality and Reliability Engineering International, 36(1), 18–32. https://doi.org/10.1002/qre.2557
  • Li, W., & Qiu, P. (2020). A general charting scheme for monitoring serially correlated data with short-memory dependence and nonparametric distributions. IISE Transactions, 52(1), 61–74. https://doi.org/10.1080/24725854.2018.1557794
  • Li, W., Xiang, D., Tsung, F., & Pu, X. (2020). A diagnostic procedure for high-dimensional data streams via missed discovery rate control. Technometrics, 62(1), 84–100. https://doi.org/10.1080/00401706.2019.1575284
  • Li, W., Zhang, C., Tsung, F., & Mei, Y. (2021). Nonparametric monitoring of multivariate data via KNN learning. International Journal of Production Research, 59(20), 6311–6326. https://doi.org/10.1080/00207543.2020.1812750
  • Li, Y., & Tsung, F. (2009). False discovery rate-adjusted charting schemes for multistage process monitoring and fault identification. Technometrics, 51(2), 186–205. https://doi.org/10.1198/TECH.2009.0019
  • Li, Y., & Tsung, F. (2012). Multiple attribute control charts with false discovery rate control. Quality and Reliability Engineering International, 28(8), 857–871. https://doi.org/10.1002/qre.1276
  • Lowry, C. A., Woodall, W. H., Champ, C. W., & Rigdon, S. E. (1992). A multivariate exponentially weighted moving average control chart. Technometrics, 34(1), 46–53. https://doi.org/10.2307/1269551
  • Mei, Y. (2010). Efficient scalable schemes for monitoring a large number of data streams. Biometrika, 97(2), 419–433. https://doi.org/10.1093/biomet/asq010
  • Moustakides, G. V. (1986). Optimal stopping times for detecting changes in distributions. The Annals of Statistics, 14(4), 1379–1387. https://doi.org/10.1214/aos/1176350164
  • Patel, H. I. (1973). Quality control methods for multivariate binomial and Poisson distributions. Technometrics, 15(1), 103–112. https://doi.org/10.1080/00401706.1973.10489014
  • Qiu, P. (2014). Introduction to statistical process control. Chapman & Hall/CRC.
  • Qiu, P. (2018). Some perspectives on nonparametric statistical process control. Journal of Quality Technology, 50(1), 49–65. https://doi.org/10.1080/00224065.2018.1404315
  • Qiu, P. (2020). Big data? Statistical process control can help! The American Statistician, 74(4), 329–344. https://doi.org/10.1080/00031305.2019.1700163
  • Qiu, P., Li, W., & Li, J. (2020). A new process control chart for monitoring short-range serially correlated data. Technometrics, 62(1), 71–83. https://doi.org/10.1080/00401706.2018.1562988
  • Qiu, P., & Xie, X. (2022). Transparent sequential learning for statistical process control of serially correlated data. Technometrics, 64(4), 487–501. https://doi.org/10.1080/00401706.2021.1929493
  • Sabahno, H., & Celano, G. (2023). Monitoring the multivariate coefficient of variation in presence of autocorrelation with variable parameters control charts. Quality Technology & Quantitative Management, 20(2), 184–210. https://doi.org/10.1080/16843703.2022.2075193
  • Song, Y., Cai, C., Ma, D., & Li, C. (2024). Modelling and forecasting high-frequency data with jumps based on a hybrid nonparametric regression and LSTM model. Expert Systems with Applications, 235, 121527. https://doi.org/10.1016/j.eswa.2023.121527
  • Wang, A., Xian, X., Tsung, F., & Liu, K. (2018). A spatial-adaptive sampling procedure for online monitoring of big data streams. Journal of Quality Technology, 50(4), 329–343. https://doi.org/10.1080/00224065.2018.1507560
  • Wang, K.-J., & Asrini, L. J. (2023). Multivariate auto-correlated process control by a residual-based mixed CUSUM-EWMA model. Quality and Reliability Engineering International, 39(4), 1120–1142. https://doi.org/10.1002/qre.3278
  • Wang, K., & Jiang, W. (2009). High-dimensional process monitoring and fault isolation via variable selection. Journal of Quality Technology, 41(3), 247–258. https://doi.org/10.1080/00224065.2009.11917780
  • Woodall, W. H., & Montgomery, D. C. (2014). Some current directions in the theory and application of statistical process monitoring. Journal of Quality Technology, 46(1), 78–94. https://doi.org/10.1080/00224065.2014.11917955
  • Xian, X., Archibald, R., Mayer, B., Liu, K., & Li, J. (2019). An effective online data monitoring and saving strategy for large-scale climate simulations. Quality Technology & Quantitative Management, 16(3), 330–346. https://doi.org/10.1080/16843703.2017.1414112
  • Xian, X., Wang, A., & Liu, K. (2018). A nonparametric adaptive sampling strategy for online monitoring of big data streams. Technometrics, 60(1), 14–25. https://doi.org/10.1080/00401706.2017.1317291
  • Xiang, D., Li, W., Tsung, F., Pu, X., & Kang, Y. (2021). Fault classification for high-dimensional data streams: A directional diagnostic framework based on multiple hypothesis testing. Naval Research Logistics (NRL), 68(7), 973–987. https://doi.org/10.1002/nav.22008
  • Xie, X., & Qiu, P. (2022). Robust monitoring of multivariate processes with short-ranged serial data correlation. Quality and Reliability Engineering International, 38(8), 4196–4209. https://doi.org/10.1002/qre.3199
  • Xie, X., & Qiu, P. (2023). Control charts for dynamic process monitoring with an application to air pollution surveillance. The Annals of Applied Statistics, 17(1), 47–66. https://doi.org/10.1214/22-AOAS1615
  • Xie, X., & Qiu, P. (2024). A general framework for robust monitoring of multivariate correlated processes. Technometrics, 66(1), 40–54. https://doi.org/10.1080/00401706.2023.2224411
  • Xue, L., & Qiu, P. (2021). A nonparametric CUSUM chart for monitoring multivariate serially correlated processes. Journal of Quality Technology, 53(4), 396–409. https://doi.org/10.1080/00224065.2020.1778430
  • Yan, H., Paynabar, K., & Shi, J. (2018). Real-time monitoring of high-dimensional functional data streams via spatio-temporal smooth sparse decomposition. Technometrics, 60(2), 181–197. https://doi.org/10.1080/00401706.2017.1346522
  • Ye, H., & Liu, K. (2022). A generic online nonparametric monitoring and sampling strategy for high-dimensional heterogeneous processes. IEEE Transactions on Automation Science and Engineering, 19(3), 1503–1516. https://doi.org/10.1109/TASE.2022.3146391
  • Ye, H., Xian, X., Cheng, J.-R. C., Hable, B., Shannon, R. W., Elyaderani, M. K., & Liu, K. (2023). Online nonparametric monitoring of heterogeneous data streams with partial observations based on Thompson sampling. IISE Transactions, 55(4), 392–404. https://doi.org/10.1080/24725854.2022.2039423
  • You, L., & Qiu, P. (2021). Joint modeling of multivariate nonparametric longitudinal data and survival data: A local smoothing approach. Statistics in Medicine, 40(29), 6689–6706. https://doi.org/10.1002/sim.9206
  • Zhang, C., Tsung, F., & Zou, C. (2015). A general framework for monitoring complex processes with both in-control and out-of-control information. Computers & Industrial Engineering, 85, 157–168. https://doi.org/10.1016/j.cie.2015.03.007
  • Zhang, W., & Mei, Y. (2023). Bandit change-point detection for real-time monitoring high-dimensional data under sampling control. Technometrics, 65(1), 33–43. https://doi.org/10.1080/00401706.2022.2054861
  • Zhao, X., Hu, J., Mei, Y., & Yan, H. (2022). Adaptive partially observed sequential change detection and isolation. Technometrics, 64(4), 502–512. https://doi.org/10.1080/00401706.2022.2124307
  • Zou, C., & Qiu, P. (2009). Multivariate statistical process control using LASSO. Journal of the American Statistical Association, 104(488), 1586–1596. https://doi.org/10.1198/jasa.2009.tm08128
  • Zou, C., Wang, Z., Zi, X., & Jiang, W. (2015). An efficient online monitoring method for high-dimensional data streams. Technometrics, 57(3), 374–387. https://doi.org/10.1080/00401706.2014.940089

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