TY - GEN
T1 - Multi-attribute data dynamics discontinuity identification
T2 - 2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014
AU - Laftchiev, Emil
AU - Lagoa, Constantino
AU - Brennan, Sean
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - Improvements in sensor technology and data processing rates are leading to the collection of vast databases of time series data. These data sets are spawning new applications that are transforming the way we live, work, and learn. In particular in the domain of vehicles, the rapid expansion of sensors to monitor both the state of the system and the occupants has led to an increase in the available data, but the research is still inconclusive on how to best handle this data. This paper develops one data representation that is scalable in dimension and efficiently stores/retrieves multi-attribute time series in the presence of noise. Here the proposed data representation is a multi-input multi-output autoregressive model (MIMO ARX) with an exogenous input. MIMO ARX models are an advantageous data representation because they are a dimension-reducing representation that inherently describes the inter-dependencies in the data while enabling the creation of efficient noise mitigation approaches. Tests using real-life vehicle data show the effectiveness of these data representations in the application of passenger vehicle localization.
AB - Improvements in sensor technology and data processing rates are leading to the collection of vast databases of time series data. These data sets are spawning new applications that are transforming the way we live, work, and learn. In particular in the domain of vehicles, the rapid expansion of sensors to monitor both the state of the system and the occupants has led to an increase in the available data, but the research is still inconclusive on how to best handle this data. This paper develops one data representation that is scalable in dimension and efficiently stores/retrieves multi-attribute time series in the presence of noise. Here the proposed data representation is a multi-input multi-output autoregressive model (MIMO ARX) with an exogenous input. MIMO ARX models are an advantageous data representation because they are a dimension-reducing representation that inherently describes the inter-dependencies in the data while enabling the creation of efficient noise mitigation approaches. Tests using real-life vehicle data show the effectiveness of these data representations in the application of passenger vehicle localization.
UR - http://www.scopus.com/inward/record.url?scp=84988246955&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988246955&partnerID=8YFLogxK
U2 - 10.1109/CDC.2014.7040276
DO - 10.1109/CDC.2014.7040276
M3 - Conference contribution
AN - SCOPUS:84988246955
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 5666
EP - 5673
BT - 53rd IEEE Conference on Decision and Control,CDC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 December 2014 through 17 December 2014
ER -