ChangePointDetection Syntax Elements - Teradata Vantage

Machine Learning Engine Analytic Function Reference

Product
Teradata Vantage
Release Number
9.02
9.01
2.0
1.3
Published
February 2022
Language
English (United States)
Last Update
2022-02-10
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B700-4003
lifecycle
previous
Product Category
Teradata Vantage™
TargetColumn
Specify the name of the input table column that contains the time series data.
Accumulate
Specify the names of the input table columns to copy to the output table.
Tip: To identify change points in the output table, specify the columns that appear in partition_exp and order_by_exp.
SegmentationMethod
[Optional] Specify the segmentation method:
   
'normal_distribution' (Default) In each segment, data is in normal distribution.
'linear_regression' In each segment, data is in linear regression.
SearchMethod
[Optional] Specify the search method, binary search.
MaxChangeNum
[Optional] Specify the maximum number of change points to detect.
Default: 10
Cost
[Optional] Specify the penalty function, which is used to avoid over-fitting:
Option Condition for Change Point Existence Condition for Normal Distribution and Linear Regression
'BIC' (Default) ln(L1)−ln(L0) > (p1-p0)*ln(n)/2 (p1-p0)*ln(n)/2 = ln(n)
'AIC' ln(L1)−ln(L0) > p1-p0

p1-p0 = 2

threshold, a DOUBLE PRECISION value

Function compares specified value to ln(L1)−ln(L0).

L1 and L0 are the maximum likelihood estimation of hypotheses H1 and H0.

For normal distribution, the definition of Log(L1 ) and Log(L0) are in ChangePointDetection (ML Engine).  

p is the number of additional parameters introduced by adding a change point. p1 and p0 represent this parameter in hypotheses H1 and H0, respectively.

OutputType
[Optional] Specify the output table columns. See ChangePointDetection Output.
Default: 'CHANGEPOINT'
Granularity
Specify the difference between the indexes of consecutive candidate change points, an INTEGER value greater than or equal to 1.
Default: 1