My aim is to forecast the daily number of registrations in two different channels.
Weekly seasonality is quite strong. Especially the difference between the weekends and the rest of the week is big. I also observe annual effects. Moreover, I have a few special event days, which significantly differ from the others days. Here is the dataset.
First, I applied a TBATS model on these two channels.
x.msts <- msts(Channel1_reg,seasonal.periods=c(7,365.25))
# fit model
fit <- tbats(x.msts)
fit
plot(fit)
forecast_channel1 <- forecast(fit,h=30)
First channel:
TBATS(0, {2,3}, -, {<7,3>, <365.25,2>})
Call: tbats(y = x.msts)
Parameters
Lambda: 0
Alpha: 0.0001804516
Gamma-1 Values: -1.517954e-05 1.004701e-05
Gamma-2 Values: -3.059654e-06 -2.796211e-05
AR coefficients: 0.249944 0.544593
MA coefficients: 0.215696 -0.361379 -0.21082
Second channel:
BATS(0, {2,2}, 0.929, -)
Call: tbats(y = y.msts)
Parameters
Lambda: 0
Alpha: 0.1652762
Beta: -0.008057904
Damping Parameter: 0.928972
AR coefficients: -0.586163 -0.676921
MA coefficients: 0.924758 0.743675
If I forecast the second channel, I only get blank values instead of any forecasts.
Could you please help why is that so?
Do you have any suggestion how to build in the specific event days into this model?
. In addition to the significant regressors (note the actual lead and lag structure have been omitted ) there were indicators reflecting the seasonality , level shifts , daily effects , changes in daily effects , and unusual values not consistent with history. The model statistics are 
. A plot of the forecasts for the next 360 days is shown here 
. The Actual/Fit/Forecast graph neatly summarizes the results 
.When faced with a tremendously complex problem (like this one!) one needs to show up with a lot of courage , experience and computer productivity aids. Just advise your management that the problem is solvable but not necessarily by using primitive tools. I hope this gives you encouragement to continue in your efforts as your previous comments have been very professional, geared towards personal enrichment and learning. I would add that one needs to know the expected value of this analysis and use that as a guideline when considering additional software. Perhaps you need a louder voice to help direct your "directors" towards a feasible solution to this challenging task.
Best Answer
Facebook has recently opened it R/Python API of 'Prophet Model'.It handles multiple level of seasonality and external event days can also be passed.Have a look-
http://machinelearningstories.blogspot.in/2017/05/facebooks-phophet-model-for-forecasting.html https://facebookincubator.github.io/prophet/docs/forecasting_growth.html