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I am very much attracted to the way stock market specialists use data mining with time series.
The data I use has multiple attributes. Each attribute has a value, a timestamp and a patient (ID) from which the value was generated at a certain moment.
The problem here is that timestamps are 24/7 without any pattern, so if I want to compare I need to shift different patients as if they happende in the same timeframe.
Which template or approach best suits this task in order to discriminate between 2 groups having a label: survived, non-survived.
I suppose I will be able to find the weights of the different attributes but I am also interested in finding the threshold where an attribut or a combiantion of attributes results in another label (eg. from survival to non-survival). These findings could facilitate the search for which attributes are important, which values of attributes determine the outcome (label) and when ( a combination of attributes, values, timestamp or frame) the label swith from survivor to non survivor or reverse.
Who is willing to give me some feedback?