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An example case: We have many observations of engine sensor time series from scenarios where a vehicle goes from idle (t0) to performing a fixed set of manoeuvres. We wish to fit a time series model for the sensor data, including some exogenous regressors.

Can anyone recommend a framework for this analysis? I've searched high and wide but am frustrated by terminology collisions with time series regressors (dynamic models) and anaylsis of repeated measures.

Bonus if recommended framework has an R package.

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Sounds like longitudinal data, first line from Wikipedia on "Longitudinal study":

A longitudinal study (or longitudinal survey, or panel study) is a research design that involves repeated observations of the same variables (e.g., people) over short or long periods of time (i.e., uses longitudinal data).

I found a book on it, Longitudinal Data Analysis for the Behavioral Sciences Using R, I don't know whether there's an associated R package.

Often the interest in longitudinal data has to do with their relationship to time-to-event data for this see Joint Models for Longitudinal and Time-to-Event Data: With Applications in R.

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