From Local Trend Extraction to Symbolization of Time-Series

International audienc

From local trend extraction to symbolization of time-series

International audienceA methodology is proposed for the extraction of local trends from a time-series. It has been designed to suit the needs of interpretation-oriented visualization from raw data. After giving implementation details for efficient computation of local trends, a characteristic analysis span is determined for each time-series. The processing results in a rich visual interpretation and a framework for the local symbolization of a time-series in terms of its value and dynamics

ICU patient state characterisation using machine learning in a time series framework

International audienceWe present a methodology for the study of real-world time-series data using supervised machine learning techniques. It is based on the windowed construction of dynamic explanatory models, whose evolution over time points to state changes. It has been developed to suit the needs of data monitoring in adult Intensive Care Unit, where data are highly heterogeneous. Changes in the built model are considered to reflect the underlying system state transitions, whether of intrinsic or exogenous origin. We apply this methodology after making choices based on field knowledge and ex-post corroborated assumptions. The results appear promising, although an extensive validation should be performed

Towards symbolization using data-driven extraction of local trends for ICU monitoring

International audienceWe propose a methodology for the extraction of local trends from a stream of data. It has been designed to suit the needs of interpretation-oriented visualization and symbolization from ICU monitoring data. After giving implementation details for efficient computation of local trends, we propose the use of a characteristic analysis span for each variable. This characteristic span is obtained from a set of criteria that we compare and evaluate in regardof analysis of ICU monitoring data gathered within the Aiddaig project. The processing results in a rich visual representation and a framework for the local symbolization of the data stream based on its dynamics