How To Configure The Anomaly Detection Agent Xmpro Data Stream Designer
Learn how to configure the Anomaly Detection Agent in the XMPRO Data Stream Designer.
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Transcript
when we are going to do here is look at
how the normally detection agent works
and how to set it up this agent is a
deep learning a script based
implementation of the anomaly detection
model I have already set up and
configured a CSV listener agent double
read the data we want to join from a CSV
file now to add the anomaly detection
agent go to the toolbox and search for
anomaly detection you will find it under
iron machine-learning click on the agent
and drag it to the canvas connect the
output end point of the csv agent to the
input end point of the anomaly detection
agent note that the default name has
been given to this agent to rename this
agent click on the white space and start
typing click somewhere else on the
canvas and click save' to configure this
agent click on it and click on configure
first make sure you using the creek
election if you want to change this
select another collection from the
dropdown they are three anomalies that
this item can be trendy to take the
first being window change reverse
direction like we can see in the first
example the downward trend or the second
half would be reported which is the last
four digits the specific data point
which will be reported depends on
certain settings that you are going to
say the second anomaly that can be
detected or sudden spikes or dips in the
data for example if we look at the
second example we can see that the first
I and the last two would be considered
as spikes of opposing value in the
training session select the learning
other home that you'd like to use
please note that ID stands for
independent identically distributed and
SSI stands for single spectrum analysis
ID and SSI are the names of the
algorithms that can be applied to the
normal is mentioned earlier then upload
your training file
if the has either checkbox is selected
the first row in this file will be used
to determine the number and names of the
fields if this is unselected the fields
in the file will be automatically
generated names
then select your separator character
there's three options available comma
semicolon and tab this is the character
that separates the fields and flower
please double check the file to make
sure that this is correct otherwise
difficult attract errors might be caused
the next value you need to provide is
the input field this is the name of the
field and the training file that
contains the initial training values
that needs to be provided to the agent
the values in this field needs to have a
data type of double then select your
input map this is the field in the data
stream that will be checked for
anomalous readings this field also needs
to contain values that have a data type
of double Advanced Options will change
based on the select algorithm I'm just
going to change the learning algorithm
to a spike algorithm if we look in the
Advanced Options sensitivity influence
our response of the algorithm is the
changes in the map value each stream
object will not actually need to be
tuned individually according to the
users need the value history only
appears for spike algorithms often spike
is reported the algorithm will allow
this number of records to pause without
reporting and other anomaly to give time
for the value to settle for instance if
the p value length is set to 5 and a
spike is detected that lasts for 3 data
points before snapping back to normal
only the initial spike will be reported
higher if the p value is set to 3 the
second spike downwards would also be
considered anomalous spike Direction can
also be set which will make the agent
look for sudden changes in the given
direction I'm going to change this to
change point
let's look at the Advanced Options again
as you can see the options have changed
a change history only appears for change
points algorithms and influences the
length required for an emerging string
before the change will be reported if
you want repeatable results incident
integer into the deterministic seed
otherwise a random seed will be chosen
every time the agent starts click on the
plane click save to run your stream
click on publish to view the live data
click on live view select anomaly
detection agent and click Save give it a
second for the data to start coming
through
you can expand the page by clicking on
maximize the agent will output once for
every event passing through the original
event plus some new fields alert which
is this column here is a double but only
shows one zero
describing if the reading is considered
anomalous or not score which is the
column here is the algorithms initial
score for the record the value which is
the field here and martingale which is
the column here have to do with the
internal calculations determining if an
alert is anomalous or not and can
usually be ignored
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