Extract relevant features from time series data 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.

Sensor reading plot - Input


Time to failure plot - Output
I think the first question you should ask before you start going off on a deep learning model is, can you tell when the failure is going to occur just by looking at a plot of your data? If you can't, then no model will help you deduce when a failure will occur. 

You shouldn't overlook some basic models also such as exponential or poisson distribution models that should model your current problem well.

Lastly, since it's a time series for rare events, some ways to gain more insight might include time between failures, identifying thresholds of the data value triggering a failure, splicing all the data into intervals and the number of failures, etc.. You should also check if the failures follow a pattern.