Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)
Box, G.E.P., Jenkins, G.: Time Series Analysis, Forecasting and Control. Holden-Day (1990)
Koop, G.: Bayesian Econometrics. J. Wiley (2003)
Lokhorst, C.: Mathematical curves for the description of input and output variables of the daily production process in aviary housing systems for laying hens. Poultry Science, 838–848 (1996)
Mertens, K., et al.: An intelligent control chart for monitoring of autocorrelated egg production process data based on a synergistic control strategy. Computers and Electronics in Agriculture, 100–111 (2009)
Mertens, K., et al.: Data-based design of an intelligent control chart for the daily monitoring of the average egg weight. Computers and Electronics in Agriculture, 222–232 (2008)
Montgomery, D.C.: Statistical Quality Control. Wiley, Hoboken (2009)
Page, E.S.: Continuous inspection schemes. Biometrika (1954)
Neter, J., Kutner, M.H., Nachtsheim, C.J., Wasserman, W.: Applied Linear Statistical Models. Irwin, Chicago (1996)
Strathe, A.B., Danfæ, A.C.: A multilevel nonlinear mixed-effects approach to model growth in pigs. Journal of Animal Science, 638–649 (2010)
Val-Arreola, D., Kebreab, E., Dijkstra, J., France, J.: Study of the lactation curve in dairy cattle on farms in central Mexico. Journal of Dairy Science, 3789–3799 (2004)
Woudenberg, S.P.D., van der Gaag, L.C., Feelders, A., Elbers, A.R.W.: Real-time adaptive problem detection in poultry. In: Proceedings of the 21st European Conference on Artificial Intelligence (ECAI). IOS Press (to appear, 2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Woudenberg, S.P.D., van der Gaag, L.C., Feelders, A., Elbers, A.R.W. (2014). Real-Time Adaptive Residual Calculation for Detecting Trend Deviations in Systems with Natural Variability. In: Blockeel, H., van Leeuwen, M., Vinciotti, V. (eds) Advances in Intelligent Data Analysis XIII. IDA 2014. Lecture Notes in Computer Science, vol 8819. Springer, Cham. https://doi.org/10.1007/978-3-319-12571-8_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-12571-8_33
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12570-1
Online ISBN: 978-3-319-12571-8
eBook Packages: Computer ScienceComputer Science (R0)