Abstract
Time series are encountered in every field of medicine. Traditional tests are unable to assess trends, seasonality, change points and the effects of multiple predictors, like treatment modalities, simultaneously. This chapter is to assess, whether autoregressive integrated moving average (ARIMA) methods are able to do all of that.
This chapter was previously published in “Machine learning in medicine-cookbook 2” as Chap. 11, 2014.
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Cleophas, T.J., Zwinderman, A.H. (2020). Autoregressive Models for Longitudinal Data (120 Mean Monthly Population Records). In: Machine Learning in Medicine – A Complete Overview. Springer, Cham. https://doi.org/10.1007/978-3-030-33970-8_35
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DOI: https://doi.org/10.1007/978-3-030-33970-8_35
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Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33969-2
Online ISBN: 978-3-030-33970-8
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