Using employee time series data to predict employee turnover (Binary Prediction using Time Series Data)
I have time series data of employee like hours worked, shift type, Overtime, no show hours, missed punch etc. What I currently do is aggregate the data of multiple time shifts to single row per employee where flags like no show, overtime are taken as sum and numerical values like worked hours, on call hours are taken as average. Then I send it to a machine learning model where it can predict per employee.
Why I haven't been able to do time series analysis like ARIMA or RNN is that it doesn't have a regression output like stock market price or other. I only have data of whether the employee was terminated after that shift or not. So the Y value remains 0 for long time in time sheet data and suddenly changes to 1 and the data ends. So this data can't be used in time series analysis.
We could set a Y value like satisfaction level after each shifts but I have no idea to implement it with the data set I have.
I could find it any where in any articles too. Can you guys help?
python machine-learning time-series data-science
edited Nov 15 '18 at 6:33
has
761517
asked Nov 14 '18 at 11:50
Bipin KC
12
add a comment |
I have time series data of employee like hours worked, shift type, Overtime, no show hours, missed punch etc. What I currently do is aggregate the data of multiple time shifts to single row per employee where flags like no show, overtime are taken as sum and numerical values like worked hours, on call hours are taken as average. Then I send it to a machine learning model where it can predict per employee.
Why I haven't been able to do time series analysis like ARIMA or RNN is that it doesn't have a regression output like stock market price or other. I only have data of whether the employee was terminated after that shift or not. So the Y value remains 0 for long time in time sheet data and suddenly changes to 1 and the data ends. So this data can't be used in time series analysis.
We could set a Y value like satisfaction level after each shifts but I have no idea to implement it with the data set I have.
I could find it any where in any articles too. Can you guys help?
python machine-learning time-series data-science
edited Nov 15 '18 at 6:33
has
761517
asked Nov 14 '18 at 11:50
Bipin KC
12
add a comment |
I have time series data of employee like hours worked, shift type, Overtime, no show hours, missed punch etc. What I currently do is aggregate the data of multiple time shifts to single row per employee where flags like no show, overtime are taken as sum and numerical values like worked hours, on call hours are taken as average. Then I send it to a machine learning model where it can predict per employee.
Why I haven't been able to do time series analysis like ARIMA or RNN is that it doesn't have a regression output like stock market price or other. I only have data of whether the employee was terminated after that shift or not. So the Y value remains 0 for long time in time sheet data and suddenly changes to 1 and the data ends. So this data can't be used in time series analysis.
We could set a Y value like satisfaction level after each shifts but I have no idea to implement it with the data set I have.
I could find it any where in any articles too. Can you guys help?
python machine-learning time-series data-science
edited Nov 15 '18 at 6:33
has
761517
asked Nov 14 '18 at 11:50
Bipin KC
12
I have time series data of employee like hours worked, shift type, Overtime, no show hours, missed punch etc. What I currently do is aggregate the data of multiple time shifts to single row per employee where flags like no show, overtime are taken as sum and numerical values like worked hours, on call hours are taken as average. Then I send it to a machine learning model where it can predict per employee.
Why I haven't been able to do time series analysis like ARIMA or RNN is that it doesn't have a regression output like stock market price or other. I only have data of whether the employee was terminated after that shift or not. So the Y value remains 0 for long time in time sheet data and suddenly changes to 1 and the data ends. So this data can't be used in time series analysis.
We could set a Y value like satisfaction level after each shifts but I have no idea to implement it with the data set I have.
I could find it any where in any articles too. Can you guys help?
python machine-learning time-series data-science
python machine-learning time-series data-science
edited Nov 15 '18 at 6:33
has
761517
asked Nov 14 '18 at 11:50
Bipin KC
12
edited Nov 15 '18 at 6:33
has
761517
asked Nov 14 '18 at 11:50
Bipin KC
12
edited Nov 15 '18 at 6:33
has
761517
edited Nov 15 '18 at 6:33
has
761517
edited Nov 15 '18 at 6:33
has
761517
761517
asked Nov 14 '18 at 11:50
Bipin KC
12
asked Nov 14 '18 at 11:50
Bipin KC
12
asked Nov 14 '18 at 11:50
Bipin KC
12
12
add a comment |
add a comment |
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