"Jamaa Ambarak" <email@example.com> wrote in message <firstname.lastname@example.org>... > First of all , I have deal with real data that used for lane predication and I have collected my data and now I want to use it to predict lane deviation of a car, I have used time series for prediction and I have selected 3 features for that and they" lateral position, steer-angle and speed velocity." the data is about 3 .5 hours of driving , I have collected data from 15 drivers and the drivers are drowses some of them they draft 200 , 360 times of the lane, so I have selected time windowing from lane deviation cases(out of the lane ) and the normal cases(in the lane) for training .the window size is 1second =50 samples and the 3 features are represented in matrix as "150x400" as example 50 samples lateral position, 50 samples speed velocity, and 50 samples steer-angle. The 400 are the other cases in one matrix. > For testing data I want to use sliding window to test whole driver. I use the sliding window for 3 features, the data is big it is about 500.000 for one driver and I want to test sample by sample for example [1 2 3 4 5 6..........150] Transpose. The second one will be >[2 3 4 5 6.....151] Transpose. And so on ? the matrix will be as [150X405316].
Have you considered having a 3-H-1 net where any combination of position, angle and velocity would correspond to a target of 0 or 1?
Have you considered the significant correlation lags between the inputs and output?
It is hard for me to believe that you really need 50 samples a second to determine lane deviation. How much susampling have you investigated?