ARIMA-based forecasting of cerebral physiologic signals in acute traumatic brain injury: a CAnadian high-resolution TBI (CAHR–TBI) cohort study

This study highlights the significant influence of temporal resolution and data partitioning methods on the predictive performance of ARIMA models for cerebral physiological signals. While ARIMA performs well at high temporal resolutions, its accuracy declines for raw physiological signals as resolution decreases. The choice of cross-validation method also impacts forecasting performance. The findings underscore the need for hybrid modeling approaches that integrate ARIMA with machine learning techniques to improve predictive accuracy, particularly for complex cerebral physiological signals.

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Publication Details

Journal
Intensive Care Medicine Experimental
Published
2026-03-02
DOI
https://doi.org/10.1186/s40635-026-00855-y
Primary Topic
Traumatic Brain Injury Research
Type
article
Field-Weighted Citation Impact
0.00

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article

ARIMA-based forecasting of cerebral physiologic signals in acute traumatic brain injury: a CAnadian high-resolution TBI (CAHR–TBI) cohort study

Françis Bernard, François Mathieu, Rahul Raj, Andreas H. Kramer et al.
Intensive Care Medicine Experimental
Traumatic Brain Injury Research
article

ARIMA-based forecasting of cerebral physiologic signals in acute traumatic brain injury: a CAnadian high-resolution TBI (CAHR–TBI) cohort study

Françis Bernard, François Mathieu, Rahul Raj, Andreas H. Kramer, Nuray Vakitbilir, Marcel Aries, Frederick A Zeiler, Eric Peter Thelin, Clare Gallagher, Kevin Y. Stein, Donald E. G. Griesdale
article en

Abstract

This study highlights the significant influence of temporal resolution and data partitioning methods on the predictive performance of ARIMA models for cerebral physiological signals. While ARIMA performs well at high temporal resolutions, its accuracy declines for raw physiological signals as resolution decreases. The choice of cross-validation method also impacts forecasting performance. The findings underscore the need for hybrid modeling approaches that integrate ARIMA with machine learning techniques to improve predictive accuracy, particularly for complex cerebral physiological signals.

Intensive Care Medicine ExperimentalVol. 14(1)
Karolinska University Hospital (SE), University of Helsinki (FI), University of British Columbia (CA), University of Calgary (CA), University of Toronto (CA), Maastricht University Medical Centre (NL), Helsinki University Hospital (FI), Karolinska Institutet (SE), Maastricht University (NL), Pan Am Clinic (CA), Hotchkiss Brain Institute (CA), University of Manitoba (CA), Université de Montréal (CA)
Health Sciences Centre Foundation, Research Manitoba, Karolinska Institutet, Vetenskapsrådet, Svenska Läkaresällskapet, Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada
Quality Education
Openalex Percentile: Top 38%
Traumatic Brain Injury Research
DOIOpen Access PDFOpenAlex
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