# How can I address missing values for LSTM?

I'm a student and writing my first paper for submission on conference. I have a question

there is a dataset below. this is temporal-spatial dataset.

Date         Hour   City       Sensor1  Sensor2  Sensor3 Sensor4 ...
21-06-10     0      Region1      0.12     0.52    0.33     0.44  ...
21-06-10     1      Region2      0.16     0.83    0.34     0.49  ...
21-06-10     2      Region1      0.21     0.44    0.57     0.5   ...
...


My Task is anomaly detection for each region

I want to use LSTM. So, I represent the temporal-spatial data to two time-series data. my dataset can be represented below.

City       Date       Hour     Sensor1  Sensor2  Sensor3 Sensor4 ...
Region1   21-06-10     0         0.12     0.52    0.33     0.44  ...
Region1   21-06-10     2         0.21     0.44    0.57     0.5   ...
...

City       Date       Hour     Sensor1  Sensor2  Sensor3 Sensor4 ...
Region2   21-06-10     1         0.16     0.83    0.34     0.49  ...
...


However, then, there is no a row with attribute 'Hour=1' in Region1 dataset (you can see the table below)

City       Date       Hour     Sensor1  Sensor2  Sensor3 Sensor4 ...
Region1   21-06-10     0         0.12     0.52    0.33     0.44  ...
Region1   21-06-10     1         NaN      NaN     NaN      NaN   ...
Region1   21-06-10     2         0.21     0.44    0.57     0.5   ...
...


Can I insert estimated values into the row with attribute 'Hour=1' in Region1 dataset? (for example, I want to insert average between the first row and the third row)

Can I claim to have utilized a real world dataset even with this missing value estimation?