I am using the forecast library for doing some time series forecasting. I need to forecast number of sold items. I am planning to add holidays as xreg in auto.arima. The holiday will be a 0/1 list, where 0 indicates no holiday and 1 indicates holiday. Holiday list has to be modeled in fourier package, which takes
fourier(x, K, h = NULL)
I am not sure how to calculate the correct value of K.
Can somebody point me to some source or some code for it in r?
This is some piece of code which I am using
holidayf <- c(0,0,0,0,0,1,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,1,0,0,0,1,1,1,1,1,0,0,0,0)
h <- length(holidayf)
h
#given holidays
holiday <- df[,2]
y <- ts(df[,1],start = 2011,frequency = 52)
z <- fourier(y, K=k)
zf <- fourier(y, K=k, h=h)
fit <- auto.arima(y, xreg=cbind(z,holiday), seasonal=FALSE)
fc <- forecast(fit, xreg=cbind(zf,holidayf), h=h)
fc %>% autoplot()
summary(fit)
```
Best Answer
often times there are significant sales before on and after each holiday . often times individual days of the month are important ... sometimes week-of-the-month .. often long weekends have. There can be daily fixed effects, monthly effects. There can be level shifts and multiple trend changes. See https://autobox.com/capable.pdf slide 50- for more on modelling daily data.
K: is the Maximum order of Fourier terms or frequencies that you wish to specify .
If you wish you can post your data and I will try and help further .