Gaussian Processes for time-marked time-series data

John Cunningham, Zoubin Ghahramani, Carl Rasmussen
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:255-263, 2012.

Abstract

In many settings, data is collected as multiple time series, where each recorded time series is an observation of some underlying dynamical process of interest. These observations are often time-marked with known event times, and one desires to do a range of standard analyses. When there is only one time marker, one simply aligns the observations temporally on that marker. When multiple time-markers are present and are at different times on different time series observations, these analyses are more difficult. We describe a Gaussian Process model for analyzing multiple time series with multiple time markings, and we test it on a variety of data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v22-cunningham12, title = {Gaussian Processes for time-marked time-series data}, author = {Cunningham, John and Ghahramani, Zoubin and Rasmussen, Carl}, booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics}, pages = {255--263}, year = {2012}, editor = {Lawrence, Neil D. and Girolami, Mark}, volume = {22}, series = {Proceedings of Machine Learning Research}, address = {La Palma, Canary Islands}, month = {21--23 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v22/cunningham12/cunningham12.pdf}, url = {https://proceedings.mlr.press/v22/cunningham12.html}, abstract = {In many settings, data is collected as multiple time series, where each recorded time series is an observation of some underlying dynamical process of interest. These observations are often time-marked with known event times, and one desires to do a range of standard analyses. When there is only one time marker, one simply aligns the observations temporally on that marker. When multiple time-markers are present and are at different times on different time series observations, these analyses are more difficult. We describe a Gaussian Process model for analyzing multiple time series with multiple time markings, and we test it on a variety of data.} }
Endnote
%0 Conference Paper %T Gaussian Processes for time-marked time-series data %A John Cunningham %A Zoubin Ghahramani %A Carl Rasmussen %B Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2012 %E Neil D. Lawrence %E Mark Girolami %F pmlr-v22-cunningham12 %I PMLR %P 255--263 %U https://proceedings.mlr.press/v22/cunningham12.html %V 22 %X In many settings, data is collected as multiple time series, where each recorded time series is an observation of some underlying dynamical process of interest. These observations are often time-marked with known event times, and one desires to do a range of standard analyses. When there is only one time marker, one simply aligns the observations temporally on that marker. When multiple time-markers are present and are at different times on different time series observations, these analyses are more difficult. We describe a Gaussian Process model for analyzing multiple time series with multiple time markings, and we test it on a variety of data.
RIS
TY - CPAPER TI - Gaussian Processes for time-marked time-series data AU - John Cunningham AU - Zoubin Ghahramani AU - Carl Rasmussen BT - Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics DA - 2012/03/21 ED - Neil D. Lawrence ED - Mark Girolami ID - pmlr-v22-cunningham12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 22 SP - 255 EP - 263 L1 - http://proceedings.mlr.press/v22/cunningham12/cunningham12.pdf UR - https://proceedings.mlr.press/v22/cunningham12.html AB - In many settings, data is collected as multiple time series, where each recorded time series is an observation of some underlying dynamical process of interest. These observations are often time-marked with known event times, and one desires to do a range of standard analyses. When there is only one time marker, one simply aligns the observations temporally on that marker. When multiple time-markers are present and are at different times on different time series observations, these analyses are more difficult. We describe a Gaussian Process model for analyzing multiple time series with multiple time markings, and we test it on a variety of data. ER -
APA
Cunningham, J., Ghahramani, Z. & Rasmussen, C.. (2012). Gaussian Processes for time-marked time-series data. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 22:255-263 Available from https://proceedings.mlr.press/v22/cunningham12.html.

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