I have a problem where I have transaction data for many banking accounts. The task is to train a model on historical debit/expense transactions and then forecast expense transactions for the next n days.
The data for each account is aggregated daily - I have total expense values for each account per day. I have done some research and found that the ConvLSTM model (proposed here) is powerful for these multistep ahead forecasts. A example of this model to forecast the next 7 days of power consumption of a household is shown at the bottom of this tutorial.
I am struggling to understand how the convolutional unit is used within the LSTM unit to make these forecasts. Specifically I am having trouble understanding the shape that my input should be in to feed it into TensorFlow's ConvLSTMCell (I think I will need to use the Conv1DLSTMCell sub-class?) and also what format the output/predictions will be returned as.