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What is MAD()

MAD() is a Vertica User Defined Transform Function using the Median Absolute Deviation. to implement a robust anomaly detection system.

We often have to deal with signals changing their statistical properties over time. Think for example to a room exposed to sunlight... we can expect the average temperature to decrease during the nights. To find "anomalies" in these cases we have to "follow" the natural drift of the signal. graph with outliers

One possible approach to find the anomalies "following" the signal is to compare its values with the Moving Average of the last N measures. The problem with Moving Averages is that the anomaly we want to identify influences the Average itself.

Suppose you have a signal like this:

sample signal

As you can see we have a few anomalies (outliers) in this graph. Now if we analyze data distribution with mean and we set a threshold at two standard deviations (2σ) , which should account 95% of the variations in a normal distribution, we get:

mean and 2sigma

as you can see only one data point exceed our threshold. But if we replace the mean with the median and the 2σ threshold with 2mad, we will get them all:

median and 2mad

Median Absolute Deviation is defined as:

MAD= Const * median ( |Xi - Xmed )

Where:

  • Xi is the i-th value in the data block
  • Xmed is the median of the data block
  • Const is a scale factor depending on input data distribution (1.4826 for "normal" distribution)

As we want to follow the signal to calculate the moving Median Absolute Deviation we should also set the size of the "rolling window" we will use calculate our MAD.

How MAD() works.

The mad() function uses the following syntax:

SELECT mad(<col>)  OVER([PARTITION BY <col1>] 
                         ORDER BY <col2> 
                         [USING PARAMETERS
                             [setsize=N] [, const=M]);

and returns the following columns:

  • rownum row number starting from 1 (it can be used to join mad() output with the source table (see Makefile)
  • median for the last setsize rows
  • mad (Median Absolute Deviation) for the last setsize rows;
  • cutoff calculated as abs(value-median)/mad of the current row with median and mad on the rolling window of the last setsize rows.

The Default Values for the function parameters, if not specified, are:

  • const = 1.4826 (scale factor fr normal distribution)
  • setsize = 10

How to install MAD()

Please check (and eventually change) the Makefile before using it. Pay special attention to the C++ compiler executable name. Then:

  • First step is to compile the source code: make
  • Then - as dbadmin - deploy the code in Vertica: make deploy
  • You can run make test to check everything is ok

Expected compile/deploy/test output:

$ make
g++-4.8 -O3 -D HAVE_LONG_INT_64 -std=c++11 -Wall -shared -Wno-unused-value -fPIC -I /opt/vertica/sdk/include -o /tmp/lmad.so lmad.cpp /opt/vertica/sdk/include/Vertica.cpp
$ make deploy
CREATE OR REPLACE LIBRARY lmad AS '/tmp/lmad.so' LANGUAGE 'C++';
CREATE LIBRARY
CREATE OR REPLACE ANALYTIC FUNCTION mad AS LANGUAGE 'C++' NAME 'MadFactory' LIBRARY lmad ;
CREATE ANALYTIC FUNCTION
CREATE OR REPLACE ANALYTIC FUNCTION cutoff AS LANGUAGE 'C++' NAME 'CutoffFactory' LIBRARY lmad ;
CREATE ANALYTIC FUNCTION
GRANT EXECUTE ON ANALYTIC FUNCTION mad(x FLOAT) TO PUBLIC ;
GRANT PRIVILEGE
GRANT EXECUTE ON ANALYTIC FUNCTION cutoff(x FLOAT) TO PUBLIC ;
GRANT PRIVILEGE
$ make test
           ts       | val  |   mad   | cutoff  | outlier
--------------------+------+---------+---------+---------
2019-10-11 09:12:00 |  3.0 |         |         |
2019-10-11 09:12:01 |  4.0 |         |         |
2019-10-11 09:12:02 |  5.0 |         |         |
2019-10-11 09:12:03 |  6.0 |         |         |
2019-10-11 09:12:04 |  7.0 |  1.4826 |  1.3490 |  
2019-10-11 09:12:05 | 21.0 |  1.4826 | 10.1174 | *
2019-10-11 09:12:06 | 10.0 |  2.9652 |  1.0117 |  
2019-10-11 09:12:07 |  9.0 |  2.9652 |  0.0000 |  
2019-10-11 09:12:08 | 24.0 |  4.4478 |  3.1476 | *
2019-10-11 09:12:09 |  3.0 | 10.3782 |  0.6745 |  
2019-10-11 09:12:10 |  3.0 |  8.8956 |  0.6745 |  
2019-10-11 09:12:11 |  5.0 |  2.9652 |  0.0000 |  
2019-10-11 09:12:12 |  8.0 |  2.9652 |  1.0117 |  
2019-10-11 09:12:13 | 31.0 |  2.9652 |  8.7684 | *
2019-10-11 09:12:14 |  8.0 |  4.4478 |  0.0000 |  
2019-10-11 09:12:15 | 23.0 |  4.4478 |  3.3725 | *
2019-10-11 09:12:16 |  9.0 |  1.4826 |  0.0000 |  
2019-10-11 09:12:17 |  4.0 |  7.4130 |  0.6745 |  
2019-10-11 09:12:18 |  3.0 |  5.9304 |  0.8431 |  
2019-10-11 09:12:19 |  2.0 |  2.9652 |  0.6745 |  
2019-10-11 09:12:20 |  1.0 |  1.4826 |  1.3490 |  
(21 rows)