Real-Time Adaptive Residual Calculation for Detecting Trend Deviations in Systems with Natural Variability

  • Steven P. D. Woudenberg
  • Linda C. van der Gaag
  • Ad Feelders
  • Armin R. W. Elbers
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8819)


Real-time detection of potential problems from animal production data is challenging, since these data do not just include chance fluctuations but reflect natural variability as well. This variability makes future observations from a specific instance of the production process hard to predict, even though a general trend may be known. Given the importance of well-established residuals for reliable detection of trend deviations, we present a new method for real-time residual calculation which aims at reducing the effects of natural variability and hence results in residuals reflecting chance fluctuations mostly. The basic idea is to exploit prior knowledge about the general expected data trend and to adapt this trend to the instance of the production process at hand as real data becomes available. We study the behavioural performance of our method by means of artificially generated and real-world data, and compare it against Bayesian linear regression.


Natural Variability Prediction Function Data Trend Process Instance Bayesian Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Steven P. D. Woudenberg
    • 1
  • Linda C. van der Gaag
    • 1
  • Ad Feelders
    • 1
  • Armin R. W. Elbers
    • 2
  1. 1.Department of Information and Computing SciencesUtrecht UniversityThe Netherlands
  2. 2.Department of Epidemiology, Central Veterinary InstituteWageningen URThe Netherlands

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