Frank Einstein
- 166
- 1
- TL;DR
- I want to know if there is a better way to obtain the correlation matrix of time-shifted series than just removing observations.
Hello everyone.
I have four thermometers which measure the temperature in four different positions. The data is distributed as a matrix, where each column is a sensor, and each row is a measurement. All measurements are made at exactly the same times, one measurement each hour. I have calculated the correlation matrix between all four positions.
Now I am interested in the calculation of the time-shifted correlation matrix. The only method I can think of is to remove the first n rows of the sensors 1 and 2 and the last n rows of the sensors 3 and 4 to see how the correlation changes.
I was wondering if there is a better way to do this than just removing rows.
Any help is appreciated.
Best regards.
Frank.
PS. I am using Python, so I have just used the function
I have four thermometers which measure the temperature in four different positions. The data is distributed as a matrix, where each column is a sensor, and each row is a measurement. All measurements are made at exactly the same times, one measurement each hour. I have calculated the correlation matrix between all four positions.
Now I am interested in the calculation of the time-shifted correlation matrix. The only method I can think of is to remove the first n rows of the sensors 1 and 2 and the last n rows of the sensors 3 and 4 to see how the correlation changes.
I was wondering if there is a better way to do this than just removing rows.
Any help is appreciated.
Best regards.
Frank.
PS. I am using Python, so I have just used the function
np.cov(Tdata_shifted2) and np.cov(Tdata) to obtain the shifted an non-shifted matrices.