Vantage 1.0 - CCM Example: Simulated Data - Teradata Vantage

Teradata® Vantage Machine Learning Engine Analytic Function Reference

prodname
Teradata Vantage
vrm_release
1.0
8.00
category
Programming Reference
featnum
B700-4003-098K
This example uses a simulated data set to show how to use the CCM function to:
  1. Identify the optimal value for EmbeddingDimensions.
  2. Check for a causal relationship between two time series.

Input

InputTable: ccmexample
seqid t a b
1 1 0.439016523 0.844698604
1 2 0.79590473 0.416313404
1 3 0.457454911 0.80120226
1 4 0.83460391 0.462840003
1 5 0.453855618 0.866674285
1 6 0.847111468 0.420195438
1 7 0.464311363 0.840110673
1 8 0.854059164 0.474302962
1 9 0.440280147 0.863834294
1 10 0.809889391 0.425037187
2 1 0.773946283 0.63958518
2 2 0.508680994 0.850617675
... ... ... ...
2 10 0.766623465 0.940756652
3 1 0.813294227 0.789552227
3 2 0.247789031 0.549992501
... ... ... ...
3 10 0.793969139 0.608697646
... ... ... ...
... ... ... ...
10 1 0.503156674 0.794651776
10 2 0.823104545 0.580508316
... ... ... ...
10 10 0.812508422 0.406036663

Step 1 SQL Call: Identify Optimal Value for EmbeddingDimensions

The CauseColumns and EffectColumns arguments must have the same value, the SelfPredict argument must have the value 'true', and the LibrarySize argument must be omitted.

SELECT * from CCM (
  ON CCMExample AS InputTable
  USING
  SequenceIDColumn ('seqid')
  TimeColumn ('t')
  CauseColumns ('b')
  EffectColumns ('b')
  EmbeddingDimensions (2,3,4,5,6,7,8,9,10)
  SelfPredict ('t')
) AS dt;

Step 1 Output

cause effect library_size correlation jaccard_index lower_bound upper_bound effect_size effect_size_id embedding_dimension
b b 90 0.997619146432549 0.997619146432549 0.997619146432549 0 0 0.997619146432549 2

Step 2 SQL Call: Check for Causal Relationship Between Two Time Series

The EmbeddingDimensions argument has the optimal value identified in Step 1.

SELECT * from CCM (
  ON CCMExample AS InputTable
  USING
  SequenceIDColumn ('seqid')
  TimeColumn ('t')
  CauseColumns ('a','b')
  EffectColumns ('a','b')
  EmbeddingDimensions ('2')
) AS dt;

Step 2 Output

cause effect library_size correlation jaccard_index lower_bound upper_bound effect_size effect_size_id
a b 3 0.0953577181959707 0.0474348274083848 0.142842631635922 0.2053062000969 0.0474348274083848 0.0261042449848043
a b 100 0.292185902147916 0.276353905641276 0.307859356247328   0.276353905641276  
b a 3 0.126132322073481 0.0748762908890498 0.176723628043906 0.495124117770105 0.0748762908890498 0.0282432749583384
b a 100 0.55247162271285 0.538744605905156 0.565905368590645   0.538744605905156