Modeling and Prediction VIs

LabVIEW 2014 Advanced Signal Processing Toolkit Help

Edition Date: June 2014

Part Number: 372656C-01

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Owning Palette: Time Series Analysis VIs

Requires: Advanced Signal Processing Toolkit. This topic might not match its corresponding palette in LabVIEW depending on your operating system, licensed product(s), and target.

Use the Modeling and Prediction VIs to build autoregressive (AR) models, autoregressive-moving average (ARMA) models, modal parametric models, and stochastic state-space models for the input time series or perform prediction based on the estimated models.

The VIs on this palette can return general LabVIEW error codes or specific Time Series Analysis error codes.

Palette ObjectDescription
Time Series ModelingBuilds dynamic models of the univariate or multivariate (vector) time series. You can specify the model orders automatically or manually when building models.
TSA AR ModelingEstimates the autoregressive (AR) model of a univariate or multivariate (vector) time series according to the method you specify. Wire data to the Xt input to determine the polymorphic instance to use or manually select the instance.
TSA AR Modeling OrderEstimates the optimal order for the autoregressive (AR) model of a univariate time series. Wire data to the Xt input to determine the polymorphic instance to use or manually select the instance.
TSA ARMA ModelingEstimates the autoregressive-moving average (ARMA) model of a univariate or multivariate (vector) time series according to the method you specify. Wire data to the Xt input to determine the polymorphic instance to use or manually select the instance.
TSA ARMA PredictionPredicts the values of a univariate or multivariate (vector) time series based on the autoregressive-moving average (ARMA) model. Wire data to the Xt input to determine the polymorphic instance to use or manually select the instance.
TSA Capon Frequency EstimatorUses the Capon method to estimate the leading frequency components of a univariate time series. The Capon method uses a nonparametric method based on finite impulse response (FIR) filters to estimate the signal spectrum. Wire data to the Xt input to determine the polymorphic instance to use or manually select the instance.
TSA Exponential PredictionPredicts the values of a univariate time series based on exponential smoothing.
TSA MA ModelingEstimates the moving average (MA) model of a univariate time series. Wire data to the Xt input to determine the polymorphic instance to use or manually select the instance.
TSA Modal Parametric ModelingEstimates the modal parametric model of a univariate or multivariate (vector) time series. The modal parameters include magnitude, phase, damping factor, and natural frequency. Wire data to the Xt input to determine the polymorphic instance to use or manually select the instance.
TSA Stochastic State-Space ModelingEstimates the stochastic state-space model of a multivariate (vector) time series. Wire data to the Xt input to determine the polymorphic instance to use or manually select the instance.

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