7.00.02 - Input - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Aster Analytics
Release Number
Release Date
September 2017
Content Type
Programming Reference
User Guide
Publication ID
English (United States)

The HMMSupervisedLearner function takes a vertices table as the input fact table. Each sequence represents a vertex. The PARTITION BY clause consists of list attributes representing the unique sequence across the entire table. The ORDER BY clause sorts the observations in each sequence chronologically in ascending order.

The function can train either one HMM or multiple HMMs. Each model id corresponds to an output HMM model.

HMMSupervisedLearner Input Table Schema
Column Data Type Description
model_column Any Identifies the set of observations in a single model.
sequence_column Any Identifies a sequence of observed values.
skip_column Any data type that can have the value "false", "no", "f", "n", "0", "true", "yes", "t", "y", or "1". Indicates rows to skip. A value of "true", "yes", "t", "y", or "1" indicates a row to skip. If the value is NULL, the row is not skipped.
state_column Any Hidden state that generates the observations.