How to detect time for the future events in time series data? I am dealing with IOT data from a mechanical machine. On the input I have ~100 features that are measured every minute. On the output, I have labels of zeros and ones, where zero indicates the absence of the event and 1 indicate the presence of an event. The event represent the failure for the machine in place. Therefore, the goal is to predict at every time step the remaining &quot;minutes&quot; for a failure to occur. I would like to know how to tackle this problem, and if possible for some material to read.
Is there a way to know which features in the past leads to a failure in the future if I'm using an LSTM?
Try boosting and random forests. You would need a long enough time series. A good reference (with examples in R) is
James, G., Witten, D., Hastie, T., &amp; Tibshirani, R. (2017). An Introduction to Statistical Learning: with Applications in R. Springer.
Usefulness of particular features can be assessed via variable importance plots.