I am struggling a bit with the logic.
I need to estimate growth based on historical data.
My data is basically composed of dates + a snapshot of disk usage.
I want to estimate the growth column to a point where I can use
disk_usage[T-1] * growth[T] = forecasted value
Data is pretty linear. So a linear model should do the job. When I do the regression in EXCEL the line looks pretty straight, not many outliers, no seasonality and the data looks stationary.
I need to do the regression in Python. When I use the statsmodel package I need to use a equation as a string :
import statsmodels.formula.api as smf model = smf.ols('???', data=dt)
In the above, the
???, should it be
growth ~ growth[T-1] or
growth ~ rowIndex or maybe I should add another column which is days since the first date in the sample ?
UPDATE: I am not looking for anything too complex. Just a informed guess. This is not a critical component of our system and even if it is not 100% accurate, no one will suffer.