Daily timeseries forecasting, with weekly and annual seasonality 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?
 A: 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
A: Ponthu, 
I took your data and ran it in Autobox.
Both of your events are important.
Months 1,2,3 and 12 are higher than the rest of the months.
Day 4 is typically 303 units higher than the other days of the week.
You can simulate this by creating 11 dummy variables for the monthly effects, 6 dummies for the day of the week, etc.
For Channel 2 if you take Feb 2010 to Jan 31 2012 and do some pivot tables on the 2 years of data you can get a ROUGH idea of how the data looks pivot tables for month, day of the week and day of the month to compare against the model. The model takes care of the jump in April using the Event2 variable.  Day 5 and 11 seem to be important, but that is just the impact of event1 and event2.  The day of the week impact changed 4 times for the 4th day of the week(monday) as you will see with the "seasonal pulses" identified. A level shift up(change in the mean ) was identified in march 2011 which went even higher in july and then was brought down a bit in november 2011.
There are a lot of outliers.  If you look at the largest ones it is clear that there is some impact around christmas and new years, but you only have 2 years of data.  I recommend 4.  You might want to consider adding in more events.
You didn't provide future values of event 1 and 2 so I didn't provide any forecasts.




