how to analyse and predict(machine learning) a time series data set using scikit-learn for python

Question

i got data-set like this

i need to analyse and predict the status column. This is just 2 entrees from the training data set. In this data set there is heart rate pattern(which is collected in 1 second intervals, 10 numbers altogether) its a time series array(correct me if i'm wrong) i just need to know best way to analyse and get a prediction using this data. I'm using scikit-learning for my data-mining and machine learning.

What i just want to know is what is the best way to analyse these time series data? should i use vector based approach or something else. If you can give me example code that would be great for me to understand it.

Answer 1

Feed in each point in the heart rate time series as a separate column, along with a separate column (feature) for all of the the other data points. Do feature normalization (substract the mean, divide by the standard deviation) for each column over the entire dataset, and feed into a classifier.