I have a time series data set from a sensor and the task is to predict the time before a failure event is occurred. The data set has one feature and has almost 20 million rows. This is a regression problem.
I tried polynomial features, auto correlation, rolling statistics and expanding statistics. The only one that seemed to improve my model was expanding sum. What are some relevant features to be extracted from this data?
My model is a Linear Regression model, the data set was scaled and currently only two features improved my model. The sensor data and the expanding sum. Any other suggestions to tackle this problem other than using deep learning?
Update: For clarification I added the plots for both the input and output.