I have my feature space that is
Year Month Day Hour Minute Second Lane Direction Speed Flag
A sample vector would look like:
[2015, 11, 5, 2, 20, 58, 4, 2, 45.7, 2]
The features take the values:
Year : 2012 - 2017
Month : 1 - 12
Day : 1 - 7
Hour - 0 - 23
Minute : 0 - 59
Second : 0 -59
Lane : 1 - 6
Direction : 1 - 2
Speed : continuous values
Flag : 1-3
My data is time series data. I am unsure of how to normalise each of these features. For example the
year feature, is it necessary to normalise them? Most of my features take discrete values.
If I do normalise them, would my SVM model perform better than when I don't normalise them?
Any suggestions on how to normalise time series data for SVM or reference to any papers will be appreciated.