The
SANN Time Series (Classification) workspace node can be accessed from the Feature Finder, the ribbon bar, or the Node Browser. The
Specifications - Quick tab is displayed by default when the specifications dialog box is opened.
| Element Name
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Description
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| Use previous input description
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Select this check box to use the variable selection specified in the Select dependent variables and predictors node, accessed via the ribbon bar or by double-clicking the data source node in the workspace. Note that the previous
Select Cases and
Weight specifications will be used also. If case selection conditions and/or weights were previously specified, you can view those specifications from this workspace dialog box, but you cannot change them (the options will be dimmed).
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| Variables
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Click the Variables button to display a variable selection dialog box. You can select one
Categorical target and one or more
Continuous and/or
Categorical inputs. Note that you should only perform time series analysis when your data involve lagged (over time) predictions.
Note: For time series analysis, selecting input variables is optional. In that case a
STATISTICA SANN time series analysis (whether regression or classification) will map future values of the target variable on its past measurements. In other words
SANN will predict future values of the target variable from its past.
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| Analysis variables (present in the dataset)
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This group box displays the variables currently selected for the analysis.
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| Strategy for creating predictive models
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Different tabs and options will be available according to the option selected in this group box.
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| Automated network search (ANS)
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Select this option button to enable the Automated Network Search (ANS), which is used for creating neural networks with various settings and configurations with minimal effort. ANS helps you create and test neural networks for your data analysis and prediction problems. It designs a number of networks to solve the problem and then selects those networks that best represent the relationship between the input and target variables. ANS is not applicable when deploying models since no training of neural networks is required.
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| Custom neural networks (CNN)
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Select this option button to use the Custom neural networks (CNN) strategy. In contrast to ANS, the Custom Neural Networks (CNN) tool enables you to choose individual network architectures and training algorithms to exact specifications. You can use CNN to train multiple neural network models with exactly the same design specifications but with different random initialization of weights. As a result, each network will find one of the possible solutions posed by neural networks of the same architecture and configurations.
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| Subsampling (random, bootstrap)
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Select this option button to use the
Subsampling (random, bootstrap) strategy. This tool enables you to create an ensemble of neural networks based upon multiple subsamples of the original data set. Options for the
Subsampling (random, bootstrap) strategy are available on the
Subsampling tab.
Options / C / W. See Common Options.
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| OK
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Click the
OK button to accept all the specifications made in the dialog box and to close it. The analysis results will be placed in the
Reporting Documents node after running (updating) the project.
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See also,
Specifications - Sampling (CNN and ANS) tab,
Specifications - Subsampling tab,
Specifications - Time Series tab,
Specifications - Quick Specification tab,
Specifications - MLP Activation Functions tab,
MLP tab,
Specifications - Weight Decay tab,
Specifications - Initialization tab,
Specifications - Real Time Training Graph tab,
Results - Samples tab,
Results - Predictions tab,
Results - Graphs tab,
Results - Details tab,
Results - Time Series tab,
Results - Code Generator tab,
Downstream tab, and
Home tab.
