Question re: 05_1_TimeSeries_HistoricalEvents!

[afogarty85]

Good Afternoon! Thank you very much for all you have done with these repositories of knowledge! I had a question about the file: 05_1_TimeSeries_HistoricalEvents.ipynb

In the case of the solution code here:

############### Solution ###############
offset = '7D'

data_window = df_train[['product_id', 'date', 'target']].groupby(['product_id', 'date']).agg(['count', 'sum']).reset_index()
data_window.columns = ['product_id', 'date', 'count', 'sum']
data_window.index = data_window['date']

data_window_roll = data_window[['product_id', 'count', 'sum']].groupby(['product_id']).rolling(offset).sum().drop('product_id', axis=1)
data_window_roll = data_window_roll.reset_index()
data_window_roll.columns = ['product_id', 'date', 'count_' + offset, 'sum_' + offset]
data_window_roll[['count_' + offset, 'sum_' + offset]] = data_window_roll[['count_' + offset, 'sum_' + offset]].shift(1)
data_window_roll.loc[data_window_roll['product_id']!=data_window_roll['product_id'].shift(1), ['count_' + offset, 'sum_' + offset]] = 0
data_window_roll['avg_' + offset] = data_window_roll['sum_' + offset]/data_window_roll['count_' + offset]
data = df_train.merge(data_window_roll, how='left', on=['product_id', 'date'])
data

We are typically left with a np.nan value for the first row of each group's avg_7D. Would you all recode this to zero, or leave it as nan and drop the row? Additionally, would you typically include several of these in your model? Say, compute 3D and a 7D offset average?

Separately, I take it you apply identical functions to the valid and test sets, as well, right?

Lastly, where/when I might learn more about similar courses that you might offer in the future?

Thank you for your time and consideration!