  For sequentially observed functional data exhibiting multiple change points
in the mean function, we establish consistency results for the estimated number
and locations of the change points based on the norm of the functional CUSUM
process and standard binary segmentation. In addition to extending similar
results in Venkatraman (1992) and Fryzlewicz (2014) for scalar data to the
general Hilbert space setting, our main results are established without
assuming the Gaussianity of the data, and under general linear process
conditions on the model errors.
