AIJul 23, 2019
TSRuleGrowth : Extraction de règles de prédiction semi-ordonnées à partir d'une série temporelle d'éléments discrets, application dans un contexte d'intelligence ambiante
arXiv:1907.10054v11 citations
Originality Synthesis-oriented
AI Analysis
It addresses the problem of mining predictive rules from time series for ambient intelligence applications, but appears incremental in method.
The paper introduces TSRuleGrowth, an algorithm for extracting partially-ordered rules from time series data, and applies it to real-world connected environment data to identify user habits.
This paper presents a new algorithm: TSRuleGrowth, looking for partially-ordered rules over a time series. This algorithm takes principles from the state of the art of rule mining and applies them to time series via a new notion of support. We apply this algorithm to real data from a connected environment, which extract user habits through different connected objects.