There are 2 parts to this question. Suppose we are looking at sales S of a product across $> 1000$ stores where a it sells. For each of these 1000 stores we have 24 months recorded data.
- We want to be able to predict S_t <- f(S_{t-1}). We could build a RNN for each of the store time series, calculate test RMSE and then take an average after taking care of normalizing values etc. But the problem is there are very few samples per time series. If we were to segment stores into groups (by say Dynamic Time Warping) then could we create a monologue of text sentiment mining where like in text two sentences are separated by a dot here we would have two time series separated by a special symbol (let's say). In that case, we would generate a RNN model on
Train_1 | Train_2 |...|Train_t
data and predict on
Test_1 | Test_2 |...|Test_t
- After this, we would like to set it up as a panel data problem where S_t <- f(x_{t1},x_{t2},...,x_{tn}). In that case should I build a separate neural network for each t and then connect the hidden layers from t -> t+1 -> t+2 ....
How should I implement these through packages like Keras/Theano/Mxnet etc.? Any help would be great!
For your first question, implementing this in MXNet Gluon is very straight forward. You can formulate your problem as an auto regression problem so that it doesn't have any dependency on the sequence length during or you can formulate it as single prediction and require a specific sequence length for S in order to predict S_t. Either way, this gluon tutorial can help get you started.