About us
Espace utilisateur
Education
INSTN offers more than 40 diplomas from operator level to post-graduate degree level. 30% of our students are international students.
Professionnal development
Professionnal development
Find a training course
INSTN delivers off-the-self or tailor-made training courses to support the operational excellence of your talents.
Human capital solutions
At INSTN, we are committed to providing our partners with the best human capital solutions to develop and deliver safe & sustainable projects.
Thesis
Home   /   Thesis   /   Natural language interactions for anomaly detection in mono and multi-variate time series using fondation models and retrieval augmented generation

Natural language interactions for anomaly detection in mono and multi-variate time series using fondation models and retrieval augmented generation

Artificial intelligence & Data intelligence Engineering sciences Instrumentation Technological challenges

Abstract

Anomaly detection in mono and multi-variate time series highly depends on the context of the task. State-of-the-art approaches rely usually on two main approaches: first extensive data acquisition is sought to train artificial intelligence models such as auto-encoders, able to learn useful latent reprensations able to isolate abnormality from expected system behaviors; a second approach consists in careful features construction based on a combination of expert knowledge and artificial intelligence expert to isolate anomalies from normal behaviors using limited examples. An extensive analysis of the literature shows that anomaly detection refer to an ambiguous definition, because a given pattern in time series could appear as normal or abnormal depending on the application domain and the immediate context within the successive observed data points. Fondation models and retrieval-augmented generation has the potential to substantially modify anomaly detection approaches. The rationale is that domain expert, through natural language interactions, could be able to specify system behavior normality and/or abnormality, and a joint indexing of state-of-the-art literature and time series embedding could guide this domain expert to define a carefully crafted algorithm.

Laboratory

Département d’Instrumentation Numérique
Service Monitoring, Contrôle et Diagnostic
Laboratoire Instrumentation Intelligente, Distribuée et Embarquée
Paris-Saclay
Top envelopegraduation-hatlicensebookuserusersmap-markercalendar-fullbubblecrossmenuarrow-down