I have multiple time series (about 200) of soil moisture behavior after saturation in different soil types. They are all the same length and nearly the same shape, differing only in their ultimate value and rate of soil moisture decline due to the effects of different soil properties.
What I need is an RNN model that can predict the time series with only one sequence as input. This RNN must be able to detect, at least internally, which of the 200 training sequences the input sequence corresponds to and then predict the next values. Is something like this possible? What I tried was to concatenate all the time series into one and I trained an RNN with 3 layers and different numbers of hidden units, but I didn't get good results. Should I increase the complexity of the model or try a new approach?