TL;DR: based on the chart below, would forecasting via X-11-ARIMA be appropriate?
I'm hoping this question (and any subsequent answers) could provide valuable insight into any other analyst considering using time-series forecasting in their work.
I have data regarding the units of my company's product vs. when each product was sold. It's fair to say that the sales in my company are cyclical, which is why I'm considering using an X-11-ARIMA model.
Is this an appropriate instance to use an X-11-ARIMA model? My understanding is that the X-11 takes seasons "into consideration," but does that mean that I'll end up further from the true forecast if the model removes the effect of seasonality?
The dates are not spaced out precisely equally. I could try to fix this, though, using two methods: (i) eliminating all weekend dates (which I'll end up doing anyway- sales on weekends won't be considered) and then indexing each business day to a "Day_n" value, or (ii) keeping all dates, and simply averaging the sales counts for any day that's missing (which I'm not crazy about doing).
By looking at the chart, one could find little spikes/outliers that happen on a reasonably consistent basis. These are "end of the month" values and I'm likely to use them in a different chart altogether, as usually a bulk of sales happen on the last day of the month.
The data shown in my plot spans the time from January 1st, 2014, through April 30th, 2017. Is 3+ years too much data for projecting values into one month from now?