So for the last few months I've been doing a lot of forecasting for my company and specifically I've been looking at monthly forecasts of total weight of different categories of products output's each month. I've been using time series models such as Arima, ETS, and tslm() within R to do my forecasting, as well as using cross validation to select a model. Over the last two days I've been presenting my results and discussing implementation of my forecasts. But, I've been asked the same question multiple times and I don't know the answer to it, so let me ask you guys. If this has been asked before, I apologize. I'll write out a few questions that hopefully will make clear what I'm trying to understand. Also, I'd like to keep technical answers about the models in the context of R, since that's what I'm using.
Do time series models take into account the number of days in a month?
Particularly, do time series models consider the number of business days in a month? (or is there a way to incorporate this?)
Do we even need to worry about this when forecasting using a time series model or does the model account for this?
For instance, lets say in October of 2014 a certain category sold 35,000 lbs of a product, and that there were 31 days, 23 of which were business days. Well for this year, 2015, there are still 31 days, but now there are only 22 business days.
Just some background on the data, I have monthly data that starts in August of 2008.
- So would it possibly be better to average the weight per month with the number of business days and forecast out this way since the # of business days change month to month and year to year?