Time augment for Google Stock forecasting experiment

[mhkim9714]

Hi, first of all thanks for introducing this fabulous work!
I have a question regarding the _process_data inside the stock.py.
As shown in https://github.com/patrick-kidger/NeuralCDE/blob/master/example/example.py, shouldn't we augment time before the features of the data?
The final data before computing the coefficients for the natural cubic spline interpolation is shaped
X : [3661,24,6]
y : [3661,1,6] (when assuming single-step forecast of lag 24 and horizon 1)
The last dimension '6' means {Open, High, Low, Close, Adj_Close, Volume} of the stock_data.csv.
I think the last dimension should be 7 including {time, Open, High, Low, Close, Adj_Close, Volume}.
If I am misunderstanding something about the NCDE implementation, please feel free to correct me.
Thanks again!