I have data since 2013 until September 2015.
I've used ARIMA and HoltWinter package in R, and they've been helpful in providing a good enough range. I backtested 2013 and 2014 data, and forecasted the first two months of 2015 in order to see the effectiveness of the models.
HoltWinter's point forecast provide a forecast with 92% accuracy; whereas ARIMA (basic
forecast() function) yielded a horrible point forecast, but the High 80 provided data that were 98% in its accuracy.
When predicting the the month of March, the forecasting wasn't accurate as predictable events changed the growth pattern. Which is currently a problem I'm trying to overcome:
If I have future data (like upcoming campaigns or events that will influence the shift), is there a way to place weights unto my data in order to attain a better point forecast, and a narrower range?
The following is my data set:
Jan Feb Mar Apr May Jun 2013 3195192781 2606422285 2594936757 2517205695 2438385639 13862430738 2014 3067035426 2560556889 2505293863 2417450086 2255966873 2555497109 2015 3507620376 2873085434 4333775070 3585868583 3041188609 3131197550 Jul Aug Sep Oct Nov Dec 2013 2925506655 2728647400 10948454069 5323935540 4125424142 3321004801 2014 2344100258 2435105729 7404847006 5027479552 4160851017 3886407118 2015 2994265048 3079366844 5991324696
The following is a graph representing the trends - jumps in September across all years, bumps in June (2013 is an outlier). However, during October, the decay will be more apparent in 2015 as no events will exist for October.
I would like to express that to the system in order to forecast better data for the months of October, November and December using weights.