Modelling longitudinal data We have longitudinal data on children(n<20) in which we measure different quantities A,B,C,D (like distance walked, time spent in school etc.). These are all continuous variables. We measure these variables daily  and we have been measuring them for more than one and half years. 
Now, based on these variables (A,B,C,D), I am trying to model individual growth trajectories of  each child.  Ideally, I want to have a single curve that explains how these variables have evolved with respect to time.  For now, I am not concerned if different child have different trajectories.
I have been studying multi-level models to model growth curves. These techniques were not helpful particularly because I am not interested in modelling individual variables separately and my time series data is quite long(n>500 for each individual). I want to find a relation between  all these variables and time.  
I have been wandering for quite a time. I am looking for getting started papers or some ideas.
 A: Functional Data Analysis probably provides the framework you are looking for. Your wording "quite long series data", "continuous variables" echoes the basic intuitions behind this approach. It actually seems you are describing a problem closely related to multivariate functional regression. 
Yao 's Functional Linear Regression Analysis for Longitudinal Data (preprint) describes in detail the theory behind this and Müller's et al. Inferring gene expression dynamics via functional regression analysis paper provides a nice test case (and presents some of the theory again). There is work on functional mixed effects regression also in case you are interested in "clustering" your data somehow (gender-wise, sibling associations etc.). 
Assuming that your "weapon of choice" is MATLAB, the package PACE provides a lot of readily available routines for regression, smoothing, warping (time-registration) and component analysis (on a regular or irregular grid) for your data. (For the ME models you will probably need to use Functional PCA and then model the projections with R's lmer()).
