Receding Horizon Prediction by Bayesian Combination of Multiple Predictors
(2012) 51st IEEE Conference on Decision and Control, 2012 p.5278-5285- Abstract
- This paper presents a novel online approach of merging multiple different predictors of time-varying dynamics into a single optimized prediction. Different predictors are merged by recursive weighting into a single prediction using regularized optimization. The approach is evaluated on two different cases of data with shifting dynamics; one example of prediction using several approximate models of a linear system and one case of glucose prediction on a non-linear physiologically based simulated type I diabetes data using several parallel linear predictors. The performance of the combined prediction significantly reduced the total prediction error compared to each
predictor in each example.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3402585
- author
- Ståhl, Fredrik LU and Johansson, Rolf LU
- organization
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proc. 51st IEEE Conf. Decision and Control (CDC 2012), December 10-13, 2012. Maui, Hawaii, USA
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 51st IEEE Conference on Decision and Control, 2012
- conference location
- Maui, Hawaii, United States
- conference dates
- 2012-12-10 - 2012-12-13
- external identifiers
-
- wos:000327200405097
- scopus:84874258116
- ISSN
- 0191-2216
- project
- DIAdvisor
- language
- English
- LU publication?
- yes
- id
- 6ed27d98-78f0-4dee-9b55-d89e585c99f8 (old id 3402585)
- date added to LUP
- 2016-04-01 14:00:07
- date last changed
- 2024-01-09 21:44:26
@inproceedings{6ed27d98-78f0-4dee-9b55-d89e585c99f8, abstract = {{This paper presents a novel online approach of merging multiple different predictors of time-varying dynamics into a single optimized prediction. Different predictors are merged by recursive weighting into a single prediction using regularized optimization. The approach is evaluated on two different cases of data with shifting dynamics; one example of prediction using several approximate models of a linear system and one case of glucose prediction on a non-linear physiologically based simulated type I diabetes data using several parallel linear predictors. The performance of the combined prediction significantly reduced the total prediction error compared to each<br/><br> predictor in each example.}}, author = {{Ståhl, Fredrik and Johansson, Rolf}}, booktitle = {{Proc. 51st IEEE Conf. Decision and Control (CDC 2012), December 10-13, 2012. Maui, Hawaii, USA}}, issn = {{0191-2216}}, language = {{eng}}, pages = {{5278--5285}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Receding Horizon Prediction by Bayesian Combination of Multiple Predictors}}, url = {{https://lup.lub.lu.se/search/files/3717227/3806436.pdf}}, year = {{2012}}, }