I have worked with time series data to predict the defect in production lines. I want to extract the feature from time series data as inputs variables for machine learning algorithms (such as support vector machine). My dataset looks like:
Timestamp Pressure t0 x0 t1 x1 t2 x2 . . . . . . tn xn
There is a threshold b. If xt>b that means the defect happened. My goals are to extract features from time series dataset above and put it into algorithms to predict the value of time stamp t
n+2 (short-term) and long-term t
n+10. However, I have not yet found the way to extract feature and how to bring the threshold into the algorithm. Could anyone suggest me how to deal with it? Thank you.