I am trying to build a model in Keras that has a variable length number of timesteps, and has a variable length number of outputs. Specifically, the data I am working with is a database of customers, where I have historical information about their activity for the last 3 years, some customers are new and some are old so I have varying length information about them. I want to be able to generate a prediction of how much each customer will spend over any arbitrary time horizon of my choosing.
I have done some research, and am wondering if anyone has any advice on the following:
Should I simply use zero padding and then use masking to handle the variable length inputs? Does this mean I also need to zero pad the outputs?
Alternatively, I suppose I could set the RNN to stateful and have a batch size = 1, and reset the state after the end of each unique customer’s history. This seems slow as I would be feeding in only 1 row at a time.
A hybrid approach - bucket my data into similar lengths and for each bucket set batch_size = bucket length, then apply zero padding and masking. However, I suppose this means I need to train a separate model on each bucket.
What is the beset way to make variable length predictions? I see a couple of ways:
feed the predictions back into the model and keep generating output
I understand that statefulness can also be kept at prediction time as well, is that true? If so is this a good approach and does anyone know more about this?