Classifying Pattern and Feature Properties to Get a $\Theta(n)$ Checker and Reformulation for Sliding Time-Series Constraints
Beldiceanu, Nicolas, Carlsson, Mats, Quimper, Claude-Guy, Restrepo-Ruiz, Maria-Isabel
–arXiv.org Artificial Intelligence
Given, a sequence $\mathcal{X}$ of $n$ variables, a time-series constraint ctr using the Sum aggregator, and a sliding time-series constraint enforcing the constraint ctr on each sliding window of $\mathcal{X}$ of $m$ consecutive variables, we describe a $\Theta(n)$ time complexity checker, as well as a $\Theta(n)$ space complexity reformulation for such sliding constraint.
arXiv.org Artificial Intelligence
Dec-3-2019
- Country:
- North America
- Canada > Quebec (0.04)
- United States > Massachusetts
- Suffolk County > Boston (0.04)
- Europe
- Sweden (0.04)
- Spain (0.04)
- Netherlands > North Brabant
- Eindhoven (0.04)
- France
- Pays de la Loire > Loire-Atlantique
- Nantes (0.04)
- Hauts-de-France > Nord
- Lille (0.04)
- Pays de la Loire > Loire-Atlantique
- North America
- Genre:
- Research Report (0.50)
- Technology: