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Corresponds to the number of points of the sliding window. The sliding window is used to convert a univariate variable to a multivariate one. The default value is 1. 

This parameter controls how fast the algorithm is going to adapt and learn changes in the incoming data flow. A low shingle (short sliding window) means that the algorithm is more flexible to changes, forgets older data faster, and tends to learn changes quickly. On the other hand, a high shingle value will not adapt fast to changes and will remember old behavior longer. One needs to find the right balance between flexibility on detecting data changes (anomalies) and the undesired tendency to learn anomalies too quickly and thus not detect them as such in the future.

The shingle value is somehow equivalent to the notion of the period and it is often chosen as a multiple of the period.

A higher number of trees leads to better estimation results. However, the improvement decreases as the number of trees increases, in other words, at a certain point the benefit in prediction performance from learning more trees will be lower than the cost in computation time for learning these additional trees. The default value is 40.

A higher tree size improves the capacity to learn more about the data. As with the Number of trees parameter, at a certain point, the cost in computation time will increase considerably. The default value is 256.

Expected age at which a random sample point should expire and be replaced by a tree. It is used to calculate the internal decay factor, which is 1/time decay. It must be greater than or equal to the tree size. The default value is 256.

This value can also be edited by dragging the light green band. The default value is 100.

Keep in mind that you may not get proper results if you enter a low value in this parameter since the algorithm would not be able to learn enough. However, indicating the widest possible range may also be dangerous, since it may cause overfitting.

Limit on the algorithm score from which the points are considered anomalies. After training the model, you can update the threshold by clicking the Update threshold button next to this option or by dragging the red horizontal line. The default value is 10.

The threshold is a constant value that decides how strict do you want your system to be with the anomalies it produces. Increasing the threshold will make the chart detect only very clear anomalies. This is useful in situations where only extremely weird data points are of importance and ambiguous ones can be discarded. By contrast, if the threshold is set to a low value, every point that differs slightly from the normality will be an anomaly. This is often the case when tiny variations are of extreme importance (for example, imagine anomalies on a certain quantity that causes a deathly disease). In this case, even though many false positives will be produced, one would prefer to catch every point that is different in any aspect. This quantity is different in every series, so that's why a deep understanding of the underlying problem is required before setting this value.