plotinerrcorr

R2026b

(To be removed) Plot input to error time-series cross-correlation

plotinerrcorr will be removed in a future release. For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.

For advice on updating your code, see Version History.

Syntax

plotinerrcorr(x,e)
plotinerrcorr(...,'inputIndex',inputIndex)
plotinerrcorr(...,'outputIndex',outputIndex)


Description

plotinerrcorr(x,e) takes an input time series x and an error time series e, and plots the cross-correlation of inputs to errors across varying lags.

plotinerrcorr(...,'inputIndex',inputIndex) optionally defines which input element is being correlated and plotted. The default is 1.

plotinerrcorr(...,'outputIndex',outputIndex) optionally defines which error element is being correlated and plotted. The default is 1.

Examples

Plot Cross-Correlation of Inputs to Errors

Here a NARX network is used to solve a time series problem.

[X,T] = simplenarx_dataset;
net = narxnet(1:2,20);
[Xs,Xi,Ai,Ts] = preparets(net,X,{},T);
net = train(net,Xs,Ts,Xi,Ai);

Y = net(Xs,Xi,Ai);
E = gsubtract(Ts,Y);
plotinerrcorr(Xs,E)

Version History

Introduced in R2010b

R2026a: plotinerrcorr will be removed

plotinerrcorr will be removed in a future release.

To learn more about visualization workflows, see these examples and topics:

• Deep Learning Visualization Methods

• Explore Network Predictions Using Deep Learning Visualization Techniques

• Deep Learning Metrics

• Monitor Deep Learning Training Progress

• ROC Curve and Performance Metrics

For more information, see Transition Legacy Neural Network Code to dlnetwork Workflows.