I am working on time series to forecast the number of inbound calls for next 1 month on a daily basis.
I built a model using
auto.arima and forecasted. My forecasting data is showing gradually increasing no. of calls irrespective of holiday or weekend.
(I have data for 2 months Aug-1-2016 to Dec-31- 2016. Here I want to forecast for next 1 month data.)
Historical data is saying that there are fewer calls on weekends and holidays (for instance week days average no. calls per day: 1200, weekend average no. of calls: 700).
http://autobox.com/cms/index.php/afs-university/intro-to-forecasting/doc_download/53-capabilities-presentation starting at slide 49 may be of some help to you. What you should do is totally ignore advice regarding ARIMA approaches as they are simply rear-window driving analogues and use (the front window ) causal structures such as day-of-the-week, week-of-the-month , month-of-the-year, day-of-the-month , week-of-the month etc… while incorporating any additionally identified latent variables such as level shifts, local time trends etc. Given your paucity of data some of which I mentioned is evidently inestimable but should be an inspiration for you to collect more data , possibly then identifying long-weekend effects, lead and lag effects around holidays etc. all while adjusting for anomalous data points.
As one of my gurus pointed out recently …..
"ARIMA, for example is fine for some kinds of data, okay for others and poor for still others; the same could be said of essentially anything else. There's no magic pill. – Glen_b "