Time Series, Path, and Attribution Analysis - Aster Analytics
Teradata AsterĀ® Analytics Release Notes
- Product
- Aster Analytics
- Release Number
- 6.21
- Published
- March 2017
- Language
- English (United States)
- Last Update
- 2018-04-13
- dita:mapPath
- kvu1474411790101.ditamap
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- AA-notempfilter_pdf_output.ditaval
- dita:id
- zdi1474411568514
- lifecycle
- previous
- Product Category
- Software
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Convergent Cross-Mapping:
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CCMPrepare. The CCMPrepare function adds a new partition column and partitions the data to prepare it for use with the CCM function.
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CCM. The CCM function tests multiple causes and effects simultaneously, reporting an effect size for each cause-effect pair.
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Shapelet Functions:
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UnsupervisedShapelet. The UnsupervisedShapelet function takes a set of time series and assigns them to clusters, based on the shapelets that it finds.
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SupervisedShapeletTrainer. The SupervisedShapeletTrainer function takes a set of classified time series and outputs a model for classifying time series, based on the shapelets that it finds.
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SupervisedShapeletClassifier. The SupervisedShapeletClassifier function takes a set of time series and assigns them to clusters, based on the model output by SupervisedShapeletTrainer.
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VARMAX
. The VARMAX function extends the ARMA/ARIMA model to work with time series with multiple response variables (vector time series), as well as exogenous variables, or variables that are independent of the other variables in the system.