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The autocorrelation coefficient formula of one-dimensional time series is as belows: enter image description here

$r_k = {\sum\limits^{n-k}_{l=1}}(x_1-\bar x)(x_{l+k}-\bar x)/{\sum\limits^n_{l=1}}(x_l-\bar x)^2$

I want to know how to calculate the autocorrelation coefficient of multi-dimensional time series, such as(five dimensions):

1 2 3 4 5

2 3 4 5 6

3 4 5 6 7

1 2 3 4 6

2 3 4 5 7

1 2 3 4 7

4 5 6 7 8

5 6 7 8 9

4 5 6 7 9

3 4 5 6 8

...

1 2 3 4 5
2 3 4 5 6
3 4 5 6 7
1 2 3 4 6
2 3 4 5 7
1 2 3 4 7
4 5 6 7 8
5 6 7 8 9
4 5 6 7 9
3 4 5 6 8

Thanks in advance.

The autocorrelation coefficient formula of one-dimensional time series is as belows: enter image description here

I want to know how to calculate the autocorrelation coefficient of multi-dimensional time series, such as(five dimensions):

1 2 3 4 5

2 3 4 5 6

3 4 5 6 7

1 2 3 4 6

2 3 4 5 7

1 2 3 4 7

4 5 6 7 8

5 6 7 8 9

4 5 6 7 9

3 4 5 6 8

...

Thanks in advance.

The autocorrelation coefficient formula of one-dimensional time series is as belows:

$r_k = {\sum\limits^{n-k}_{l=1}}(x_1-\bar x)(x_{l+k}-\bar x)/{\sum\limits^n_{l=1}}(x_l-\bar x)^2$

I want to know how to calculate the autocorrelation coefficient of multi-dimensional time series, such as(five dimensions):

1 2 3 4 5
2 3 4 5 6
3 4 5 6 7
1 2 3 4 6
2 3 4 5 7
1 2 3 4 7
4 5 6 7 8
5 6 7 8 9
4 5 6 7 9
3 4 5 6 8

Thanks in advance.

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Bumped by Community user
Bumped by Community user
Bumped by Community user
Bumped by Community user
Bumped by Community user
Bumped by Community user
Bumped by Community user
Bumped by Community user
Bumped by Community user
Bumped by Community user
Bumped by Community user
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How to calculate the autocorrelation coefficient of multi-dimensional time series

The autocorrelation coefficient formula of one-dimensional time series is as belows: enter image description here

I want to know how to calculate the autocorrelation coefficient of multi-dimensional time series, such as(five dimensions):

1 2 3 4 5

2 3 4 5 6

3 4 5 6 7

1 2 3 4 6

2 3 4 5 7

1 2 3 4 7

4 5 6 7 8

5 6 7 8 9

4 5 6 7 9

3 4 5 6 8

...

Thanks in advance.