Python - how to normalize time-series data
I'm not going to give the Python code, but the definition of normalizing, is that for every value (datapoint) you calculate "(value-mean)/stdev". Your values will not fall between 0 and 1 (or 0 and 100) but I don't think that's what you want. You want to compare the variation. Which is what you are left with if you do this.
I have a dataset of time-series examples.  I want to calculate the similarity between various time-series examples, however I do not want to take into account differences due to scaling (i.e. I want to look at similarities in the shape of the time-series, not their absolute value).  So, to this end, I need a way of normalizing the data.  That is, making all of the time-series examples fall between a certain region e.g [0,100].  Can anyone tell me how this can be done in python