i posted a similar, but more confusion question already. I have a weekly timeseries so far, which looks like this (pls ignore the red line):
My original data is (e.g.):
index y 1 9657 2 3693 3 18063
I decided to run linear regression for this timeseries. Where I am using the previous Index as features (e.g.):
index y y1 y2 1 9657 nan nan 2 3693 9657 nan 3 18063 3693 9657
My idea is to only forecast the next period (one step ahead), so I always know y1,y2. My first question: 1. Is it allowed to use linear regression for this approche, cause I am using not indepented variables?
After I runned linear regression I was quite unsatisfied with the results, and as you can see from the figure it is not really a stationar timeseries, so i log my full data, basically all y values:
y=log(y) and on top I changed every y (also including the y) to
y= y-yprev, which should make the data even more smooth (so
y = y-y1,
Then I used the first 70 entries to train and the last 30 to test (My full prediction line(also on the training set) looks like this:
This looks quite ok now, I would try to use it. But
2. How can i convert the y_pred back to the original data size?
3. Does it makes sense at all?