I am trying to build a deep learning neural model using keras and tensorflow which can predict if a certain event will occur based on a set of timeseries data and some fixed data together . For example: For a given set of entities say , Their price behavior is co-related due to rumor of some event that is going to happen in future.
1) A & B 's fixed data like type, group etc.
2) During a certain period of time - 1 Jan 2015 - 30 Mar 2015 , their price .
Data that I have is
INPUT : Name of entity, Type Of entity , Size , Country, Specific Attributes and time series stock data from 1 Jan 2015 - 30 Mar 2015
OUTPUT : Y/N . Boolean output if event happend or not.
Now my question is how do I build this since I have some fixed data which doesn't changes over time and some time series data which changes over time.
Options that I thought of are 1) LSTM - But not sure if I should feed in fixed static data. 2) CNN - Not sure if it is the right approach ?
Please let me know what should be my approach to handle such a problem.