Quanti cation of individual magnetic resonance spectroscopy (MRS) signals is possible in the time domain using interactive nonlinear least-squares tting methods which provide maximum likelihood parameter estimates under certain assumptions or using fully automatic, but statistically suboptimal black-box methods. In kinetic experiments time series of consecutive MRS spectra are measured in which some of the parameters of the signals like e.g. frequencies or dampings are known to remain constant over time. The purpose of this paper is to show how AMARES and HTLS, two representative examples of the previously mentioned methods, can be extended to the simultaneous processing of all spectra in the time series using the common information present in the spectra. We show that this approach yields statistically better results than processing the individual signals separately.