# How to estimate the not available observation in time series data?

Suppose, I have a 30 seconds time-step observations of sports data, in some of the intervals the game was partially/fully stopped. I'm trying to prep the data for a time series analysis. Is it justified to take it as zero when it was stopped fully? or I have to interpolate the value...

Here is the sample of data created without taking DeadBallMin(game paused) into account...

** columns A and B are actual data observed during the time-step.

** Exp_Win_A and Exp_Win_B are monotonic increasing. And assume all the features are uniformly distributed within the time-step.

    A      B    Exp_win_A   Exp_win_B   DeadBallMin
0   1      0    0.891713    1.074992    0.000000
1   0      1    0.893859    1.076465    0.000000
2   0      1    0.930300    1.077941    0.036633
3   0      1    0.932539    1.112289    0.000000
4   0      0    0.934783    1.122372    0.907834


From the above table, in the second row, the game was stopped for 33.67% of the time-step.

Question

Any suggestions on how to incorporate the 'DeadBallMin' time into Exp_win_A & Exp_win_B while keeping the behaviour?

• Is high precision estimation favorable for you? – Alireza Zolanvari Mar 14 '19 at 11:57
• Yes, I can work with that. – Abs Mar 14 '19 at 12:22