I am trying to use R to build a time-series model to predict a variable which is only reported on irregular intervals. I have data for independent variables for all time periods (days), however the key dependent variable, reported_UV, is only reported monthly (every 30 or so days).
I am looking for help building this model so that reported_UV can be predicted based upon previous cases of reported_UV as well as the independent variables.
The dataset is at the following link in .csv format: https://docs.google.com/file/d/0B42_3jCtgxwYRVp5dkJ2NGVnaWs/edit?usp=sharing
A description of the data fields is as follows:
- Month_Year The Month & Year (e.g. Jun-11)
- Date The Date (e.g. 6/1/2011)
- DayInMonth The day in month (e.g. for June 1st, this would be "1")
- Weekday Shows as "1" if the day is a weekday
- Weekend Shows as "1" if the day is a weekend
- Holiday Shows as "1" if the day is a holiday
- Month Shows the month in the year (e.g. for June, this would be "6")
- daily_UV The total unique visitors for the day
- p28_UV A lagging indicator showing the total unique visitors for past 28 days. This is a deduped version of the previous 28 days of daily_UV.
- reported_UV This is the key dependent variable to predict, reported by an independent auditor. This is reported on the last day of the month and shows the total unique visitors for the month. For example, the value on June 30th shows the entire month of June. All other values are missing.