I have some daily time series data. I am trying to predict the next 3 days from the historical daily set of data.
The historical data shows a definite trend based upon the day-of-week such as Monday, Tuesday, etc Monday and Tuesdays are high, Wednesday typically highest and then decreasing over the remaining part of the week.
If i group the data monthly or weekly, i can definitely see a trend over time going up that appears to be additive.
My goal is to predict the next 3 days only. My intuition is telling me to take one approach and I am hoping for some feedback on pros/cons versus other approaches.
My intuition tells me it might be better to group the data by week or month and then predict the next week or month. Suppose I predict the next week total by loading historical weekly data into ARIMA, train, test and predict the next week. Within a week, each Day-of-week typically contributes x percent to that weekly total. So, if Wednesday historically has on average contributed 50% of the weekly volume, and for the next week I am predicted 1000, then I would predict Wednesday to be 500. Is this a common approach?
Alternatively, I could load the historical daily values into ARIMA, train, test and let ARIMA predict the next 3 days. The big difference here is the whole "predict weekly" versus "predict daily".
In the time series forecasting space, is this a common debate and if so, perhaps someone can suggest some key words i can google to educate myself on pros/cons?
Also, perhaps there is a suggested algorithm to use when day of week is a factor?
Thanks in advance for any responses. Dan
