Feature extraction in time series data as input variables for machine learning algorithms 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 tn+1, tn+2 (short-term) and long-term tn+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.
You don’t need to extract any features. You should feed the pressure data to a LSTM network. LSTM is a type of recurrent ANN that can be used to predict time series.