I have to forecast daily page views of a web portal. We have the daily page views of the data for the last 2 years. We have to forecast for next 90 days. I am using a multi-seasonal (with season values weekly (7) and annual (365.25)) TBATS model in R to forecast the data set. Is this the best model or any other model can be considered ?
A good place to start is here .. where a simple question is answered in a thorough fashion / When you have a complicated problem/opportunity sometimes simple is simply not good enough. In my opinion your choice for analysis is way too simple as it doesn't often treat the opportunities ( as listed below ) that may exist in order for a forecasting model to be useful.
Then might want to review https://stats.stackexchange.com/search?q=user%3A3382+daily+data as they detail the advantage of actually analyzing and detecting evidented structure in daily data which may include:
1) Daily deterministic effects AND/OR Daily memory effects
2) Day-of-the-month-effects and changes in day-of-the-month effects
3) Week=of-the-month effects
4) Month=of-the-year effects
5) lead and lag effects of each type of holiday
6) multiple level shifts
7) multiple time trends
8) changes in parameters over time
9) changes in error variance over time
10) long weekend effects
11) arima structure in a thorough manner
12) lead and lag effects of user-specified causals e.g. price of the paper
Software is available in R to accomplish these things .
Are you satisfied that your current approach is capturing all the information ?. This can be confirmed if tour model's residuals are free of structure .. if not then there is work to be done ! If you wish you can post your data and I will try and help further .
EDITED AFTER RECEIPT OF YOUR MODEL'S NON-RANDOM RESIDUALS:
Clearly not white noise as there are huge outliers and visually obvious variance changes . Your test for randomness i.e. a series free of both arima structure , outliers and or variance/parameter changes is simply not performing what you think it should perform. Note that untreated outliers often deflate the acf ! See Ord's citation here Determining parameters (p, d, q) for ARIMA modeling reflecting on Alice-in-Wonderland.
EDITED AFTER RECEIPT OF YOUR DATA:
This is a plot of the original 1090 daily values ... . The model that was automatically developed was (1,1,0)(0,0,0)7 is here and here . With pulse indicators representing points of inflection/unusual data points given the arima model.
The plot of the model residuals is here suggesting even more anomalies. I would have to say that this residual plot is better/cleaner than your residual plot.
You the OP have some explaining to do as to how this series was recorded as it is primarily a step up and down function not readily amenable to even the best analytical program for time series data.