"Support Vector Machines - Data Input"
Hi,
right now i try to create a simple forecasting model with svm which should be able to recognise seasonality or other influences on my, lets say, sales.
I already studied the windowing operator which splits up my training data in different time elements, like that:
variables |label
v1 v2 v3 v4 v5 |v6
v2 v3 v4 v5 v6 |v7
v3 v4 v5 v6 v7 |v8
...
The first question: Should i transform the input? If have data like 1,5,12,0,0 ... lets say, these are sales. Should i scale it between 0 and 1?
In addition i want to let my model know if something special happened. So i create a new attribute, like u which contains the information of the christmas holidays.
variables |label
v1 v2 v3 v4 v5 u1 u2 u3 u4 u5 |v6
v2 v3 v4 v5 v6 u2 u3 u4 u5 u6 |v7
e.g. v: 1,5,12,0,0 u: 1,1,1,0,0
in this case, u should tell me that the first 3 days of my time period (sales 1,5,12) where sales influenced by the christmas holidays.
Is this the right way to do it? If i scale v to between 1 and 0, and i have the binary variable u with 0 and 1, the svm can handle that well?
Next question would be the following: the christmas influence is not just 1 and 0, if i want to build a seasonal development within the
christmas sales, how i would do that? Adjust the values like 0.2, 0.3, 0.4?
And, i use a window of 5 days to predict the next day, so the window would not cover the whole season. Is there the danger that, if for
a long time there where no christmas sales, and i train my model on a regular basis, it "forgets" about christmas?
If i solved all my problems above and i have a new model which thinks about christmas and everthing could give a good hint for forecasting,
i know must tell the model for which situation it should forecast. Let's say, i have:
variables/situation |prediction
v1 v2 v3 v4 v5 u1 u2 u3 u4 u5 |f6
v2 v3 v4 v5 v6 u2 u3 u4 u5 u6 |f7
How i tell the model for f6 there will be christmas? Because with u i just describe the past?
Greetings
Martin
