I have a mechanical problem as kind of a time series with raw data as follows
time dtime cur dcur type proc start end
122088 1554207711521 3140 0.766106 0.130276 0 87556 1554203520000 1554207720000
122089 1554207714411 1800 0.894529 0.089670 0 87556 1554203520000 1554207720000
For every proc, there is a time series with time-instances not exactly in proper intervals. I have data from a set of different procs, each coming from the same type of mechanical problem. The target is to predict the estimated time left in the process from a new random instance of a random process.
So my label is eta = end - time.
I have tried clustering the raw data and use NN regression; and dense NN regression from raw data. But the results are not good enough.
I am thinking of using an LSTM RNN for the time prediction. But I am not sure how exactly should I prepare my data to train the LSTM model. I am guessing I have to create a time-series from each proc. But then I have multiple time-series and I do not know how to handle that.
Length of data samples: 122000
Number of uniques procs: 68 (samples per proc are not equal)
Suggestions are welcome. Thanks .