This work has aimed to develop a method which can be used in order to detect anomalies in signaling data streams at a telecommunication company. It has been done by modeling and evaluating three prediction models and two detection methods. The prediction models which have been implemented are Autoregressive Integrated Moving Average (ARIMA), Holt-Winters and a Benchmark model, furthermore have two detection methods been tested; Method 1 (M1), which is based on a robust evaluation of previous prediction errors and Method 2 (M2), which is based on the standard deviation in previous data. From the evaluation of the work, we could conclude that the best performing combination of prediction- and detection methods was achieved with a modified Benchmark model and M1- detection.