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Owning Palette: Time Series Analysis VIs

Installed With: 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 Object	Description
Time Series Modeling	Builds dynamic models of the input univariate or multivariate (vector) time series. You can specify the model orders automatically or manually when building models.
TSA AR Modeling Order	Estimates the optimal order for the autoregressive (AR) model of an input univariate time series. Wire data to the Xt input to determine the polymorphic instance to use or manually select the instance.
TSA AR Modeling	Estimates the autoregressive (AR) model of an input 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 Modeling	Estimates the autoregressive-moving average (ARMA) model of an input 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 Prediction	Predicts the values of an input 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 Estimator	Uses the Capon method to estimate the leading frequency components of an input 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 Prediction	Predicts the values of an input univariate time series based on exponential smoothing.
TSA MA Modeling	Estimates the moving average (MA) model of an input univariate time series. Wire data to the Xt input to determine the polymorphic instance to use or manually select the instance.
TSA Modal Parametric Modeling	Estimates the modal parametric model of an input 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 Modeling	Estimates the stochastic state-space model of an input 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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