Using finite transducers for describing and synthesising structural time-series constraints

Nicolas Beldiceanu 1, 2 Mats Carlsson 3 Rémi Douence 4 Helmut Simonis 5
1 TASC - Theory, Algorithms and Systems for Constraints
Inria Rennes – Bretagne Atlantique , Département informatique - EMN, LINA - Laboratoire d'Informatique de Nantes Atlantique
4 ASCOLA - Aspect and composition languages
LINA - Laboratoire d'Informatique de Nantes Atlantique, Département informatique - EMN, Inria Rennes – Bretagne Atlantique
Abstract : We describe a large family of constraints for structural time series by means of function composition. These constraints are on aggregations of features of patterns that occur in a time series, such as the number of its peaks, or the range of its steepest ascent. The patterns and features are usually linked to physical properties of the time series generator, which are important to capture in a constraint model of the system, i.e. a conjunction of constraints that produces similar time series. We formalise the patterns using finite transducers, whose output alphabet corresponds to semantic values that precisely describe the steps for identifying the occurrences of a pattern. Based on that description, we automatically synthesise automata with accumulators, as well as constraint checkers. The description scheme not only unifies the structure of the existing 30 time-series constraints in the Global Constraint Catalogue, but also leads to over 600 new constraints, with more than 100,000 lines of synthesised code.
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Constraints, Springer Verlag, 2016, 21 (1), 〈10.1007/s10601-015-9200-3〉
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Nicolas Beldiceanu, Mats Carlsson, Rémi Douence, Helmut Simonis. Using finite transducers for describing and synthesising structural time-series constraints. Constraints, Springer Verlag, 2016, 21 (1), 〈10.1007/s10601-015-9200-3〉. 〈hal-01370322〉

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