TSA Moving Average VI

Owning Palette: Preprocessing VIs

Requires: Advanced Signal Processing Toolkit

Performs moving average on a univariate or multivariate (vector) time series to smooth out fluctuations or to estimate the trend of the time series. Wire data to the Xt input to determine the polymorphic instance to use or manually select the instance.

Details  Example

Use the pull-down menu to select an instance of this VI.

Select an instance TSA Moving Average (Waveform)TSA Moving Average (Array)TSA Vector Moving Average (Waveform)TSA Vector Moving Average (Array)

TSA Moving Average (Waveform)

	Xt specifies the univariate time series.
	method specifies the filter type to use in filtering the input time series. Refer to the Details section for information about available options.
	error in describes error conditions that occur before this node runs. This input provides standard error in functionality.
	user defined weights specifies a set of weights for the moving average operation. If you specify a value for user defined weights, this VI ignores the setting for method. The number of weights must be odd, and the weight array must be symmetric so no phase distortion takes places in the moving average operation.
	Xt out returns the filtered univariate time series.
	error out contains error information. This output provides standard error out functionality.

TSA Moving Average (Array)

	Xt specifies the univariate time series.
	method specifies the filter type to use in filtering the input time series. Refer to the Details section for information about available options.
	error in describes error conditions that occur before this node runs. This input provides standard error in functionality.
	user defined weights specifies a set of weights for the moving average operation. If you specify a value for user defined weights, this VI ignores the setting for method. The number of weights must be odd, and the weight array must be symmetric so no phase distortion takes places in the moving average operation.
	Xt out returns the filtered univariate time series.
	error out contains error information. This output provides standard error out functionality.

TSA Vector Moving Average (Waveform)

	Xt specifies the multivariate (vector) time series.
	method specifies the filter type to use in filtering the input time series. Refer to the Details section for information about available options.
	error in describes error conditions that occur before this node runs. This input provides standard error in functionality.
	user defined weights specifies a set of weights for the moving average operation. If you specify a value for user defined weights, this VI ignores the setting for method. The number of weights must be odd, and the weight array must be symmetric so no phase distortion takes places in the moving average operation.
	Xt out returns the filtered multivariate time series.
	error out contains error information. This output provides standard error out functionality.

TSA Vector Moving Average (Array)

	Xt specifies the multivariate (vector) time series. Each column of the 2D array represents a vector at certain time.
	method specifies the filter type to use in filtering the input time series. Refer to the Details section for information about available options.
	error in describes error conditions that occur before this node runs. This input provides standard error in functionality.
	user defined weights specifies a set of weights for the moving average operation. If you specify a value for user defined weights, this VI ignores the setting for method. The number of weights must be odd, and the weight array must be symmetric so no phase distortion takes places in the moving average operation.
	Xt out returns the filtered multivariate (vector) time series. Each column of the 2D array represents a vector at certain time.
	error out contains error information. This output provides standard error out functionality.

TSA Moving Average Details

This VI estimates the trend of a time series by using a linear filtering operation according to the following equation:

where {a_r} is a set of weights. In practice, . This operation is a moving average or finite impulse response (FIR) filter.

Spencer's 15-Point Moving Average

The symmetric weights are as follows:

Henderson's Moving Average

This moving average aims to follow a cubic polynomial trend without distortion. You can specify different numbers of weights based on the degree of irregularity of a time series.

The symmetric weights of 7-term are as follows:

[–0.05874 0.05874 0.29371 0.41259 0.29371 0.05874 –0.05874]

The symmetric weights of 9-term are as follows:

[–0.04072 –0.00987 0.11847 0.26656 0.33114 0.26656 0.11847 –0.00987 –0.04072]

The symmetric weights of 13-term are as follows:

[–0.01935 –0.02786 0.00000 0.06549 0.14736 0.21434 0.24006 0.21434 0.14736 0.06549 0.00000 –0.02786 –0.01935]

The symmetric weights of 23-term are as follows:

[–0.00428 –0.01092 –0.01569 –0.01453 –0.00495 0.01343 0.03893 0.06830 0.09740 0.12195 0.13832 0.14406 0.13832 0.12195 0.09740 0.06830 0.03893 0.01343 –0.00495 –0.01453 –0.01569 –0.01092 –0.00428]

Example

Refer to the Moving Average VI in the labview\examples\Time Series Analysis\TSAGettingStarted directory for an example of using the TSA Moving Average VI.