Forecast next year from time series data in R Im just starting using R and have been getting through a number of tutorials on Forecasting as need a forecast for next year. I have been using a seasonality of 7 & 365.25 in order to take account of seasonality. 
m_tbats = tbats(head(Desktop$Sessions,-1,seasonal.periods=c(7,365.25)))
f_tbats = forecast(m_tbats, h=365)
plot(f_tbats)


As you can see my forecast for next year end in a straight line, what am I doing wrong? Can I get a decent forecast if I only have 1.2 years  of data?
 A: It would appear that you have daily data that might have some buildup between Thanksgiving and Christmas BUT that is just based upon a visual examination and a SWAG (SWA Guess). This can be handled by employing a Transfer Function model incorporating seasonal dummy indicators , day-=of-the-week effects and ARIMA structure all while dealing with anomalies and other unspecified possible deterministic structure. If you post your data,I might be able to help further. Specify country, starting date and the kind of data that this represents.
It is possible with good analytics to get plausible forecasts from as little as 1.2 years of daily data. See this question R Time Series Forecasting: Questions regarding my output  as it relates to hybrid models which use both ARIMA and Deterministic structure.
Look at ARIMA - Regressor Effects to illustrate the seasonal buildup approaching Christmas/
A: It appears to me that Desktop$Sessions first needs to be formatted as a time series object using the ts() function. You should try the following and see if that produces more reasonable forecasts:
m_tbats = tbats(ts(Desktop$Sessions, frequency=7),seasonal.periods=c(7,365.25))
f_tbats = forecast(m_tbats, h=365)
plot(f_tbats)

