I'm currently facing a problem for which I am given several time series that all describe the feature.
E.g. the height of several trees of the same kind was measured over a period of time each. However, the time periods are rather random and do not necessarily overlap. For instance for the height of tree 1 we know that it was 2m on 01.01.2000, 2.1m on 08.05.2000, 3m on 06.12.2006. For tree 2 we know that its height was 2.5m on 17.03.2004 and 2.9m on 16.06.2006. this is just an example and my data is far more complex, contains a whole lot more data points and time series, but that's basically the nature of my problem.
My aim is to find a function (not yet clear if it should be linear, exponential, etc.) as a prognosis of the height of this particular sort of tree that best fits my data. However, since the dates where the heights were measured do not at all coincide and also the heights at which measurements were started are not alike, I basically have no clue how to approach this challenge.
Unfortunately I have never worked with advanced statistics or data analysis before and googling e.g. "analysis of multiple time series" does not yield anything I could work with.
Is there anyone here who's familiar with such analysis of time series? I bet there must be plenty of similar problems and lots of approaches to tackle them. I would be grateful for any suggestions! :)
Thanks a lot!!!