We have three years of data for online visits at a daily level. We want to forecast the daily visits for the next 90 days. What would be the best method to capture weekday seasonality , holiday seasons, and also the drift.
Can this be successfully done in R? We are currently using R. We have considered ARIMA but it does not capture seasonality.
While converting the data to a time series in R, what should be the "frequency"?
Should we use ARIMA with regressors?
Best Answer
In addition to what has been said, you might want to consider structural time series models. They account explicitly for one or more seasonalities and trend, and are very tolerant of missing data. A good starting point might be the R function StructTS(). More complex models can be fit with packages such as
dlm,KFAS, and several others.