# Residuals in double seasonal exponential smoothing

I have a time series with muliple seasonal cycles, which are 24 and 168 hours for my case. I would like to use Double Seasonal Exponential Smoothing method to forecast, which was published by James W. Taylor (http://users.ox.ac.uk/~mast0315/). The link for his article is here

When I check Taylor's paper and all other papers in the literature, I saw that AR(1) adjustment has been implemented to the residuals. Is it a coincidence to make AR(1) correction to residuals of all the time series, that have been used in these papers?

I am using "forecast" package in R by Rob Hyndman, http://cran.r-project.org/web/packages/forecast/forecast.pdf

Firstly, I run the code without using an AR(1) model for the errors.

    data <- scan("data.dat", skip=1)
datatimeseries <- ts(data)
library(forecast)
dataforecasts <- dshw(datatimeseries, 24, 168, armethod=FALSE)
res=dataforecasts\$residuals
acf(res)
pacf(res)


Below, you can see the ACF and PACF graphs of the residuals.

As you can see there are some serious non-zera autocorrelation. I can not do AR(1) correction as papers suggest because PACF does not show it.I do not know what to do. I am sorry if it's a silly question however I am so new to time series. Would you please care to explain what to do next step-by-step and elaborately. Thanks in advance

The AR(1) error would help a lot. But if you want something more general and flexible, use the tbats function instead.