Two seasonal periods in ARIMA using R

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I'm currently using R to predict a time series with these instructions:

1. X <- ts(datas, frequency=24)
   
2. X.arima <- Arima(X, order=c(2,1,0), seasonal=c(1,1,1))
   
3. pred <- predict(X.arima, n.ahead=24)
   
4. plot.ts(pred$pred)

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As you can see I've data each hour, and I chose the seasonal period of 24 (one day).

I would like to improve my forecasting using an additional seasonal period in order to include the seasonal component of the week (seasonal length of 7*24=168 data)

Is there any method for this? How do you do it?

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关键词：seasonal Periods period ARIMA Using seasonal periods

There are no R packages that handle multiple seasonality for ARIMA models as far as I know. You could try the forecast package which implements multiple seasonality using models based on exponential smoothing. The dshw, bats and tbats functions will all handle data with two seasonal periods.

http://www.research.att.com/techdocs/TD_100381.pdf.Automatic forecasting procedures are common in business practice where large number of time series are needed for forecast. One of such applications is on mobility network resource planning which requires accurate prediction of future peak usage at each cell tower location within the network. In this paper, we developed an automatic procedure based on univariate double seasonal ARIMA models (DSARIMA) to forecast time series database with multiple seasonal patterns. A large scale empirical study comparing automatic DSARIMA with double seasonal Exponential Smoothing (DSEXP) is performed using real mobile phone network data. We also considered the performance of combined forecasts of the two models based on OLS and variations. The results show that automatic DSARIMA models and combined forecast outperform DSEXP, especially in the forecasting horizon beyond one day ahead.