Anomaly detection is a way to use historical data to identify unusual observations without requiring a labeled training set. Modern anomaly detection methods take into account long-term trends and cyclical variation in the data while determining which observations to flag as anomalies.
Twitter has recently released an advanced open source anomaly detection package for R called AnomalyDetection. It is geared toward detecting anomalies in single value high frequency (less than a day) time series data; however, it is possible to set an option to handle datasets longer than a month. It can also be used on a vector of non-time series data.
It is good at handling the effects of trends and seasonality - although seasonality, in this case, is at the minutes to days level not yearly. The GitHub page is located here (https://github.com/twitter/AnomalyDetection), and it can be installed easily as an R package using the following R code. Make sure to spell Anomaly with a capital...