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Adaptive Prediction Interval for Data Stream Regression

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Advances in Knowledge Discovery and Data Mining (PAKDD 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14647))

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Abstract

Prediction Interval (PI) is a powerful technique for quantifying the uncertainty of regression tasks. However, research on PI for data streams has not received much attention. Moreover, traditional PI-generating approaches are not directly applicable due to the dynamic and evolving nature of data streams. This paper presents AdaPI (ADAptive Prediction Interval), a novel method that can automatically adjust the interval width by an appropriate amount according to historical information to converge the coverage to a user-defined percentage. AdaPI can be applied to any streaming PI technique as a postprocessing step. This paper develops an incremental variant of the pervasive Mean and Variance Estimation (MVE) method for use with AdaPI. An empirical evaluation over a set of standard streaming regression tasks demonstrates AdaPI’s ability to generate compact prediction intervals with a coverage close to the desired level, outperforming alternative methods.

Y. Sun—I would like to acknowledge the support from TAIAO project.

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References

  1. Barber, R.F., Candes, E.J., Ramdas, A., Tibshirani, R.J.: Predictive inference with the jackknife+ (2021)

    Google Scholar 

  2. Bifet, A., et al.: Moa: massive online analysis, a framework for stream classification and clustering. PMLR (2010)

    Google Scholar 

  3. Breiman, L.: Bagging predictors. Mach. Learn. 24, 123–140 (1996)

    Article  Google Scholar 

  4. Fanaee-T, H., Gama, J.: Event labeling combining ensemble detectors and background knowledge. Progr. Artif. Intell. 2, 113–127 (2014)

    Article  Google Scholar 

  5. Friedman, M.: A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 11(1), 86–92 (1940)

    Article  MathSciNet  Google Scholar 

  6. Gomes, H.M., Barddal, J.P., Ferreira, L.E.B., Bifet, A.: Adaptive random forests for data stream regression. In: ESANN (2018)

    Google Scholar 

  7. Gomes, H.M., Montiel, J., Mastelini, S.M., Pfahringer, B., Bifet, A.: On ensemble techniques for data stream regression. In: 2020 IJCNN. IEEE (2020)

    Google Scholar 

  8. Hadjicharalambous, M., Polycarpou, M.M., Panayiotou, C.G.: Neural network-based construction of online prediction intervals. Neural Comput. Appl. 32(11), 6715–6733 (2020)

    Article  Google Scholar 

  9. Hahn, G.J., Factors for calculating two-sided prediction intervals for samples from a normal distribution. J. Am. Stat. Assoc. (1969)

    Google Scholar 

  10. Hahn, G.J., Nelson, W.: A survey of prediction intervals and their applications. J. Qual. Technol. 5(4), 178–188 (1973)

    Article  Google Scholar 

  11. Ikonomovska, E., Gama, J., Džeroski, S.: Learning model trees from evolving data streams. Data Min. Knowl. Disc. 23, 128–168 (2011)

    Article  MathSciNet  Google Scholar 

  12. Khosravi, A., Nahavandi, S., Creighton, D., Atiya, A.F.: Lower upper bound estimation method for construction of neural network-based prediction intervals. IEEE Trans. Neural Netw. 22(3), 337–346 (2010)

    Article  Google Scholar 

  13. Liu, Y., Zhao, J., Wang, W., Pedrycz, W.: Prediction intervals for granular data streams based on evolving type-2 fuzzy granular neural network dynamic ensemble. IEEE Trans. Fuzzy Syst. 29(4), 874–888 (2020)

    Article  Google Scholar 

  14. Nix, D.A., Weigend, A.S.: Estimating the mean and variance of the target probability distribution. In: ICNN’94. IEEE (1994)

    Google Scholar 

  15. Shafer, G., Vovk, V.: A tutorial on conformal prediction. J. Mach. Learn. Res. 9(3) (2008)

    Google Scholar 

  16. Shrestha, D.L., Solomatine, D.P.: Machine learning approaches for estimation of prediction interval for the model output. Neural Netw. 19(2), 225–235 (2006)

    Article  Google Scholar 

  17. Sun, Y., Pfahringer, B., Gomes, H.M., Bifet, A.: SOKNL: a novel way of integrating k-nearest neighbours with adaptive random forest regression for data streams. Data Min. Knowl. Discov. (2022)

    Google Scholar 

  18. Waugh, S.G.: Extending and benchmarking cascade-correlation: extensions to the cascade-correlation architecture and benchmarking of feed-forward supervised artificial neural networks. Ph.D. thesis, University of Tasmania (1995)

    Google Scholar 

  19. Xu, C., Xie, Y.: Conformal prediction interval for dynamic time-series. In: International Conference on Machine Learning, pp. 11559–11569. PMLR (2021)

    Google Scholar 

  20. Zhao, J., Wang, W., Sheng, C., Zhao, J., Wang, W., Sheng, C.: Industrial prediction intervals with data uncertainty. In: Data-Driven Prediction for Industrial Processes and Their Applications, pp. 159–222 (2018)

    Google Scholar 

  21. Zhao, X., Barber, S., Taylor, C.C., Milan, Z.: Interval forecasts based on regression trees for streaming data. Adv. Data Anal. Classif. 15, 5–36 (2021)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. AI Institute, The University of Waikato, Hamilton, New Zealand

    Yibin Sun, Bernhard Pfahringer, Heitor Murilo Gomes & Albert Bifet

  2. School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

    Heitor Murilo Gomes

  3. LTCI, Télécom Paris, IP Paris, France

    Albert Bifet

Authors
  1. Yibin Sun
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  2. Bernhard Pfahringer
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  3. Heitor Murilo Gomes
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  4. Albert Bifet
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Corresponding author

Correspondence to Yibin Sun .

Editor information

Editors and Affiliations

  1. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  2. Microsoft Research Asia, Beijing, China

    Xing Xie

  3. National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  4. Duke University, Durham, NC, USA

    Jian Pei

  5. National Cheng Kung University, Tainan, Taiwan

    Jen-Wei Huang

  6. Silesian University of Technology, Gliwice, Poland

    Jerry Chun-Wei Lin

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Sun, Y., Pfahringer, B., Murilo Gomes, H., Bifet, A. (2024). Adaptive Prediction Interval for Data Stream Regression. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14647. Springer, Singapore. https://doi.org/10.1007/978-981-97-2259-4_10

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  • DOI: https://doi.org/10.1007/978-981-97-2259-4_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2261-7

  • Online ISBN: 978-981-97-2259-4

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