When you want to detect anomalies in data it is not sufficient to just distinguish data being anomalous or not for certain point in time. Anomaly indicator is designed so that it can answer also the question "how much is this data point anomalous?".
It is a number in interval (0, infinity) returned for each data point specified in model building and anomaly detection datetime ranges (except small amount of data points in the beginning of each range where detection can't be done because of model offsets). Threshold is in the number 1 - if the indicator is below or equal to 1 we say the data point is not anomalous, if it is above we say it is anomalous. The higher the number the more anomalous that particular data point is.
Anomaly indicator is closely related to sensitivity parameter. By selecting sensitivity 'x' you are basically saying that you expect 'x'% of anomalies on model building datetime ranges which causes the anomaly indicator to exceed the threshold on exactly 'x'% of these ranges. Anomaly indicator with such sensitivity is then used for detecting anomalies on anomalyDetection ranges - here in general higher sensitivities will result in anomaly indicator exceeding the threshold more often than sensitivities closer to 0.