Timeline for How can a univariate seasonal time series be made normally distrubuted by Box-Cox transformation?
Current License: CC BY-SA 3.0
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| when toggle format | what | by | license | comment | |
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| Apr 28, 2015 at 18:38 | history | edited | javlacalle | CC BY-SA 3.0 |
removed duplicated piece of code
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| Apr 26, 2015 at 20:25 | comment | added | Dirk | library(PolynomF),library(polynom) f1=as.function(as.polynomial( fit $varresult$ X1$ fitted.values)). This function I want to use for back testing for the first 12th entry, and then for the second 12th. But I think the best solution is going to be with a sarima model although the residuals are non-normally distributed. | |
| Apr 26, 2015 at 20:20 | comment | added | Dirk | thx. The VAR fit brings the problem of insignificance of the estimates (only the first estimates are significant). I fitted also p=2, the problem of insignificance exists only for the variable X1 for first column values and the other columns are ok. But I dont think that the assumption of zero for the other columns will make things better. Because every coefficient in the 11th degree polynomial has a contribution to the curvature. I want to calculate MAPE for back testing but the total observation number is 15, the observation number is 14 and so the number of fitted values. | |
| Apr 26, 2015 at 17:17 | history | answered | javlacalle | CC BY-SA 3.0 |