This question builds on my previous question Forecasting Hourly Time Series based on previous weeks and same period in previous year/s. My project is to forecast the number of ~400 different types of events expected in each hourly interval with enough accuracy for staffing decisions to be made.
Based on my knowledge of the data I know that each interval is related to the same hour band from the previous few weeks and the same time in the previous few years. Thanks to a comment by Rob Hyndman I am now using
tbats() to forecast with mixed results. When comparing the forecasted data to the actual data the monthly totals are consistently within 1-3%.
However, when I compare the forecast to the actual for individual intervals the results are not very reliable at all. I have calculated the difference between the actual and the forecast as a percentage of the forecast for each interval and get an interquartile range of 50% to 150% with a mean difference of ~70%. This level of accuracy is unacceptable for what I need to use the data to do.
I am pretty certain that there are correlations between the frequency of different types of events and some measurable environmental factors. Is there an easy way to feed R a time series for the count of each event type as well as some environmental factors and have it find the correlations and create a forecast?
I am not trying to be lazy, the forecast tool is going to be automated and needs to be able to run without human input.
The method I am currently using is:
data <- scan("data.csv") fcast <- forecast(tbats(msts(data, seasonal.periods=c(168,8766))),1464)
The csv is a single column containing an hourly count of a specific event type over 2 years.