For a student project, we got data like this:
- A one dimensional time series
- A classification label
- Two further features/parameters, unrelated to the time series
At the end of the time series there is an event characterized by two features/parameters (in total these two features give 10 different combinations). The time series consists of 5000 values (roughly between -200 and +200) and may be forming some kindle of wave.
The question is now, how to predict/classify, if the result after this event is positive or negative, given the previous time series.
We wanted to try to use a RNN, but we dont know, how to handle these 2 features of the event best, since it is not part of the time series. Simply adding these two parameters as further inputs does not seem to be appropriate as their importance is then underrepresented in respect to mass of input from the time series.