I have a time series with daily observations over the course of multiple years (interest in topic "superbowl" over time). The seasonality in the data is yearly as well and it is very spiky (almost nothing all year and big increase/spike in January/February). I have started using R for this task (
forecast package) and have little experience with statistics.
x <- ts(myts, frequency=365) fit <- HoltWinters(x) plot(forecast(fit))
This works great and captures the seasonality of the data.
Now, I have read more about exponential smoothing (at http://otexts.com/fpp/7/) and understood that the HoltWinters model is one instance of the state space models implemented in ets. Unfortunately, I could not use ets so far since it complains about the high data frequency. I definitely need daily forecast (on the order of 30-60 steps).
fit <- ets(x, 'AAA') Error in ets(x, "AAA") : Frequency too high
Why can HoltWinters deal with this but not ets? Is there a good workaround? I have the same problem for seasonal ARIMA models and considered splitting up the data in years and using past years as exogenous input.
On a side note: How do you usually deal with leap days that screw up your 365 day period? Simply delete them?
Thank you very much!
PS: I am aware of this: http://robjhyndman.com/researchtips/longseasonality/ However, I couldn't get it too work well on my data, yet. On the other hand, HoltWinters worked fairly well.
Thanks for all the helpful comments and discussion. I uploaded the data at http://timalthoff.de/data/data.zip The plot below shows Super_bowl.dat.
I took the liberty of including more time series if you'd like to check out more examples.
At certain points in time I want to forecast the time series on the order of 60 days. These points in time usually are on the left flank of a big spike that represents a sudden interest in a topic. See example.png for an example (the vertical red lines are these points in time to start an out-of-sample forecast). For more info check out the README.