Solved – Approaches to Forecasting with Daily Timeseries

Question

I have just started to learn about forecasting. I thought it would be easy to create forecast models for a daily time series but have encountered a number of difficulties. Firstly most examples and available datasets are either in months or quarters. It is rare to find examples for weeks and days. Secondly it also appears difficult to create a timeseries object for days (365) and weeks (52) as these vary between years. This may just be the way the timeseries object works in R. I have had to use Zoo. I also have a concern that my data may not be properly modeled for use in packages like Forecast and HTS.

I am interested in how best to approach this problem. Any examples of forecasting to daily events that may cycle across years would be greatly appreciated.

Answer 1

Daily data is often impacted by 1) day-of-the-week-profiles and changes in these profiles ; 2) week of the year ; 3) Time trends ( note the plural ) ; 4) Level Shifts ; 5) monthly effects ; 6) particular days of the month E.G. the first,15th etc ; 7) Lead, contemporaneous and lag effects around known events e.g Christmas , JUly4th etc ) ; 8) Unusual values ; 9)Long weekends around events ; 10) particular week in month effects ; ARIMA structure reflecting unspecified stochastic input series

That should be enough to start with.

When you think that your equation deals effectively with the above then start to consider the impact of parmeters that change over time and an error variance that may change over time.