I aim to have some anomaly detection process on my data but Weka, Rapidminer or Knime do not support anomaly detection algorithms. How would I take care of the process?
Venturini, A. (2011). Time Series Outlier Detection: A New Non Parametric Methodology (Washer). Statistica 71: 329-344.
R has a full task view listing the major implementations.
AUTOBOX http://www.autobox.com/cms/ has a selection of anomaly detection procedures including Pulse , Level/Step Shift, Time Trends ans Seasonal Pulses. These are available for both ARIMA and Transfer Function Models (XARMAX). One can specify both level of confidence and the minimum size of the anomaly that one wishes to identify. The user can also specify the minimum # of required values in a level shift. There is an R version of AUTOBOX. A distinguishing feature of AUTOBOX is that it can detect anomalies without the user having to specify a model.