Python - how to normalize time-series data

Question

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

Answer 1

Assuming that your timeseries is an array, try something like this:

(timeseries-timeseries.min())/(timeseries.max()-timeseries.min())

This will confine your values between 0 and 1

Answer 2

The solutions given are good for a series that aren’t incremental nor decremental(stationary). In financial time series( or any other series with a a bias) the formula given is not right. It should, first be detrended or perform a scaling based in the latest 100-200 samples.
And if the time series doesn't come from a normal distribution ( as is the case in finance) there is advisable to apply a non linear function ( a standard CDF funtion for example) to compress the outliers.
Aronson and Masters book (Statistically sound Machine Learning for algorithmic trading) uses the following formula ( on 200 day chunks ):

V = 100 * N ( 0.5( X -F50)/(F75-F25)) -50

Where:
X : data point
F50 : mean of the latest 200 points
F75 : percentile 75
F25 : Percentile 25
N : normal CDF