Autobox(my company) provides outlier detection. Twitter's algorithm gets the big outliers, but misses the smaller ones compared to Autobox.
It takes a long time to run, but the results are better for finding the smaller outliers and also changes in the seasonality which are also outliers. Below is the model finding 79 outliers using the first 8,560 observations of 14,398 original observations. The standard version max's out at 10,000 observations, but it could be modified for more, but there is no real reason to have that much data anyway when you want to identify and respond to outliers.
We were influenced by the work done by Tsay on outliers, level shifts, and variance change and Chow's work on parameter changes along with our own work on detecting changes in seasonality,
If you download the 30 day trial and load in the Twitter example data and specify the frequency to be 60 and save 3 trigger files in the installation folder (noparcon.afs, novarcon.afs, notrend.afs) and create a file called stepupde.afs with 100.