MATLAB: How to predict the remaining useful life of machine using neural network

Deep Learning Toolboxforecastingneural networkpredictionpreparetstime series

Hi,
1) I am trying to use the time series tool for bearing forecasting from an historic record of bearing data. I have a 2803 raw data signal which i have got from online. I'm trying to use Graphic user Interface(GUI),initially i go through some worked examples where i observed is that ,we are suppose to load input and target data before training the network.. but i fail to understand how to divide and prepare input and target data from the raw data, to load for training the network..
2) I have totally 2803 RMS data. out of which i'm taking data points from 1 to 2750 for training purpose, later by using those trained network, suppose to predict the next 53 point of time series(i.e 2751, 2752….. 2803).. Is this can be done using GUI…??? or should i use command function….???
i have a doubt , can a neural network can predict the next 30 points after 2803 (i.e 2803… to… 2833)….???if so how.. ??
please help me..

Best Answer

Find the significant lags of the autocorrelation function. The maximum significant lag tends to be the prediction limit. If you want to predict further ahead, use a sliding window feedback delay.
See examples in
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