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I have a weekly dataset and I have to normalize this data. Data is something like this :

1. week   50
2. week   51
3. week   50
4. week   54
5. week   150
6. week   155
7. week   ...

The important thing is, the difference between week 3 and week 4 (50-54) is not same with week 5 and week 6. And also there is a huge different between week 4 and week 5.

My question is how can i handle all of this things ?

Is the standard normalization functions(for example scikit normalization) can do it for me and should I normalize this data 0-1 or -1 to 1 ?

Sklearn normalization page

NOTE I am working with python and generally scikit-learn library.

Any help is appreciated.

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    $\begingroup$ What range you normalise to would depend on what you wish to do with the transformed data. $\endgroup$ – image_doctor Feb 2 '15 at 11:50
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I would find the unit variance of the all the weeks and then divide by that. Scikit can do this for you using scale.

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  • $\begingroup$ Could you please explain more detaily. I will give the list of my data(an array) to scale() function and it will return an array. Then should I divide my real list of data to the other array(which is the result of scale) ? $\endgroup$ – Batuhan Bardak Jan 29 '15 at 20:52

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