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?". Separate anomaly indicator is returned for each detection feature that was used for model building, hence the question is answered for each feature individually.
The anomaly indicator is a number in interval (0, infinity) returned for each data point on model building or detection period (except small amount of data points in the beginning of each data range where detection can't be done because of model offsets). The number 1 is the anomaly indicator threshold - 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 indicators are closely related to sensitivity parameters. Each anomaly indicator has corresponding sensitivity parameter which can be set manually or detected automatically. By selecting sensitivity 'x' you are basically saying that you expect 'x'% of anomalies on model building period which causes the anomaly indicator to exceed the threshold on exactly 'x'% of these ranges. Model with such sensitivity is then used for detecting anomalies on out of sample ranges - here in general a higher sensitivity will result in anomaly indicator exceeding the threshold more often than a sensitivity closer to zero.