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This section explains each of the properties in the configuration blade.


This associates the Agent to a specific Collection (default to that of the current Data Stream).
Refer to Collections and Stream Hosts to understand more about Collections.


Learning Algorithm
The algorithm options used to train the model are:
  • IID Change Point
  • SSA Change Point
  • IID Spike
  • SSA Spike
Training File
The formatted file that contains data to train the learning algorithm.
Has Header
Tick to auto-populate the feature names from the first row of the training file, or the features are named Column1, Column2, etc.
Separator Character
The character options to separate data values in the training file are:
  • Comma (,)
  • Semicolon (;)
  • Tab
Input Field
The training file attribute that provides training data to the algorithm.
Input Map
The column or attribute of the input payload that is examined by the algorithm. It must be of type Double.

Advanced Options

These options affect the training process of the Machine Learning algorithm.
How sensitive the algorithm is to change between values. Accepts values between 0 and 100.
Change History Length
The size of the sliding window for computing the p-value (applies to Change Point Learning Algorithms only).
P-Value History Length
The size of the sliding window for computing the p-value (applies to Spike Learning Algorithms only).
Training Window Size
The number of points from the beginning of the sequence used for training (applies to SSA Learning Algorithms only).
Seasonality Window Size
An upper bound on the largest relevant seasonality in the input time series (applies to SSA Learning Algorithms only).
Spike Direction
Select whether positive, negative, or both positive and negative jumps in value will trigger an alert (applies to Spike Learning Algorithms only).

Model Options

Deterministic Seed
An optional positive whole number for deterministic results - i.e. the exact same output guaranteed for the exact same inputs across multiple runs.
Before configuring the AI & ML Agent, please ensure that its input endpoint is connected to a parent Agent which will be sending data to it.


This endpoint is used to receive data from the parent Agent.
Events received from the parent Agent are made available to this endpoint, with the results of the Machine Learning Algorithm appended to the original event: Alert, Score, PValue, and for some models, Martingale.