data frequency conversion problem

Question

salva am 31 Jul. 2012

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Direkter Link zu dieser Frage

https://de.mathworks.com/matlabcentral/answers/44968-data-frequency-conversion-problem

Akzeptierte Antwort: Oleg Komarov

Dear all,

Is there any function for converting data that are available every 2 months to monthly data

thank you

EDIT [01 Aug 2012, 19:33 BST - OK] Added example input from comment

Here is an example of a panel data set with 3 individuals

A = {
  '1-2 2004'      0.256       0.385
  '3-4 2004'      0.268      3.0394
  '5-6 2004'     0.0504      0.6475
  '7-8 2004'    14.0985    148.2583
  '9-10 2004'     0.1128      1.1506
  '11-12 2004'        NaN    148.2583
  '1-2 2005'        NaN    148.2583
  '3-4 2005'     2.5852     34.0146
  '5-6 2005'      0.322      3.2846
  '7-8 2005'    14.0985    148.2583
  '9-10 2005'     2.5852         NaN
  '11-12 2005'     0.2938       2.854
  '1-2 2004'      0.256       0.385
  '3-4 2004'      0.268      3.0394
  '5-6 2004'     0.0504      0.6475
  '7-8 2004'    14.0985    148.2583
  '9-10 2004'     0.1128      1.1506
  '11-12 2004'        NaN    148.2583
  '1-2 2005'        NaN    148.2583
  '3-4 2005'     2.5852     34.0146
  '5-6 2005'      0.322      3.2846
  '7-8 2005'    14.0985    148.2583
  '9-10 2005'     2.5852         NaN
  '11-12 2005'     0.2938       2.854
  '1-2 2004'      0.256       0.385
 '3-4 2004'      0.268      3.0394
  '5-6 2004'     0.0504      0.6475
  '7-8 2004'    14.0985    148.2583
  '9-10 2004'     0.1128      1.1506
  '11-12 2004'        NaN    148.2583
  '1-2 2005'        NaN    148.2583
  '3-4 2005'     2.5852     34.0146
  '5-6 2005'      0.322      3.2846
  '7-8 2005'    14.0985    148.2583
  '9-10 2005'     2.5852         NaN
  '11-12 2005'     0.2938       2.854}

Note that I have 30000 invividuals (instead of 3) and 20 numerical columns instead of the last 2 that I display above. The interpolation should be done for each i=1,2,3 separately.

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salva am 1 Aug. 2012

Thank you oleg.

Here is an example of a panel data set with 3 individuals

A = {

    'MA 2009'              [       0.256]    [          0.385]    
     'MJ 2009'             [     0.2680]    [     3.0394]     
    'JA 2009'               [     0.0504]    [     0.6475]    
     'SO 2009'            [    14.0985]    [   148.2583]     
    'ND 2009'              [     0.1128]    [     1.1506]     
     'JF 2010'                [    NaN]    [   148.2583]         
     'MA 2010'                   [     2.5852]    [    34.0146]    
     'MJ 2010'                [     0.3220]    [     3.2846]    
    'JA 2010'               [    14.0985]    [   148.2583]     
    'SO 2010'                     [     2.5852]    [    NaN]     
    'ND 2010'                  [     0.2938]    [     2.8540]     
     'JF 2011'                   [     0.1128]    [     1.1506]     
     'MA 2011'                 [    14.0985]    [   148.2583]    
     'MJ 2011'                [     2.1091]    [    15.0233]     
    'JA 2011'           [          2.43]    [         3.1]     
     'MA 2009'             [    14.0985]    [   148.2583]     
     'MJ 2009'               [     2.7827]    [    18.9879]     
    'JA 2009'                    [    11.8755]    [   126.4359]    
     'SO 2009'                  [     0.0589]    [     0.6685]     
     'ND 2009'                  [    11.8755]    [   126.4359]     
    'JF 2010'                   [     0.0504]    [     0.6475]     
     'MA 2010'                  [    11.8755]    [   126.4359]     
     'MJ 2010'             [     0.0504]    [     0.6475]     
    'JA 2010'            [         3.56]    [          7.21]     
     'SO 2010'             [     0.0248]    [     0.2823]     
    'ND 2010'        [          4.21]    [          9.370]     
     'JF 2011'             [     2.5852]    [    34.0146]     
    'MA 2011'            [     0.0207]    [     0.2282]     
    'MJ 2011'                   [    11.8755]    [   126.4359]     
     'JA 2011'                 [    14.0985]    [   148.2583]     
     'MA 2009'                [     2.1091]    [    15.0233]     
    'MJ 2009'                   [          0]    [          0]     
    'JA 2009'                    [     0.1128]    [     1.1506]    
     'SO 2009'                   [     0.0207]    [     0.2282]     
     'ND 2009'               [          5.56]    [          3.56]     
     'JF 2010'                   [    NaN]    [     1.1506]     
     'MA 2010'                    [          0]    [          0]     
    'MJ 2010'                     [     2.1091]    [    15.0233]     
     'JA 2010'                    [          0]    [          0]     
    'SO 2010'                   [     2.7827]    [    NaN]     
    'ND 2010'                   [          0]    [          0]     
     'JF 2011'                     [     0.0207]    [     0.2282]     
    'MA 2011'                      [     2.5852]    [    34.0146]    
    'MJ 2011'                   [          0]    [          0]     
     'JA 2011'                    [    11.8755]    [   126.4359]     
  }
 Note that I have 30000 invividuals (instead of 3) and 20 numerical columns instead of the last 2 that I display above. The interpolation should be done for each i=1,2,3 separately.

thank you so much

salva am 1 Aug. 2012

These data are stocks, So interpolation is needed. It would be wrong to divide them by two

thank you oleg

salva am 2 Aug. 2012

Is it possible to have some code as a help?

thanks

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Answer 1

Oleg Komarov am 2 Aug. 2012

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Direkter Link zu dieser Antwort

https://de.mathworks.com/matlabcentral/answers/44968-data-frequency-conversion-problem#answer_55316

Bearbeitet: Oleg Komarov am 4 Aug. 2012

EDIT#2 I didn't notice at first that it had different series. Added also linear interpolation of NaNs:

% Inpaint NaNs and keep numeric matrix (easier to work than with cell % arrays) with inpaint_nans() from the FEX.

data = inpaint_nans(cell2mat(A(:,3:4)),2);

% Partition interpolation in blocks (first column)

blocks     = [A{:,1}];
unBlocks   = unique(blocks);

% Preallocate

interpData = cell(numel(unBlocks),1);

% Interpolate each block

for b = unBlocks
    idxBlock      = b == blocks;      % index the block
    n             = nnz(idxBlock)*2;  % counts its length
    interpData{b} = interp1((1:2:n)', data(idxBlock,:),(1:n-1)');
end

% This plot gives an idea of the type of interpolation (for the first block/series only)

subplot(311)
plot(1:2:n,[A{1:15,3}],'-or')
legend('original')
subplot(312)
plot(1:n-1,interpData{1}(:,1)','-db')
legend('linear interp')
subplot(313)
plot(1:2:n,[A{1:15,3}],'-or',1:n-1,interpData{1}(:,1)','-db')

As you can see, you need to decide what to do with NaN's (especially to avoid losing info)

% Concatenate the cells into one numeric matrix (optional)

interpData = cat(1,interpData{:});

WARNING: with this approach I assume every month has same length.

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salva am 4 Aug. 2012

Bearbeitet: salva am 4 Aug. 2012

Thank you Oleg for your reply. Regarding how to treat NaNs I would like to interpolate over them before changing the data frequency. That is, we can just replace them via interpolation.

I found some material on this

http://www.mathworks.de/matlabcentral/answers/34346-interpolating-nan-s

http://www.mathworks.de/matlabcentral/answers/22739-interpolating-missing-data

The second link is maybe what I am looking for

Having done that, then we can change the frequency.

thank you