Abstract
Integer time series are often subject to constraints on the aggregation of the integer features of all occurrences of some pattern within the series. For example, the number of inflexions may be constrained, or the sum of the peak maxima, or the minimum of the peak widths. It is currently unknown how to maintain domain consistency efficiently on such constraints. We propose parametric ways of systematically deriving glue constraints, which are a particular kind of implied constraints, as well as aggregation bounds that can be added to the decomposition of time-series constraints [5]. We evaluate the beneficial propagation impact of the derived implied constraints and bounds, both alone and together.
We thank the anonymous referees for their helpful comments. The authors in Nantes are supported by the EU H2020 programme under grant 640954 for project GRACeFUL and by the Gaspard-Monge programme. The authors in Uppsala are supported by the Swedish Research Council (VR) under grants 2011-6133 and 2012-4908. The last author is supported by Science Foundation Ireland (SFI) under grant SFI/10/IN.1/I3032; the Insight Centre for Data Analytics is supported by SFI under grant SFI/12/RC/2289.
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Arafailova, E. et al. (2016). Systematic Derivation of Bounds and Glue Constraints for Time-Series Constraints. In: Rueher, M. (eds) Principles and Practice of Constraint Programming. CP 2016. Lecture Notes in Computer Science(), vol 9892. Springer, Cham. https://doi.org/10.1007/978-3-319-44953-1_2
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