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Preprocess Data

Remove means, offsets, and linear trends; reconstruct missing data, change data sampling rate

Functions

detrendSubtract offset or trend from data signals
retrendAdd offsets or trends to data signals
diffDifference signals in iddata objects
idfiltFilter data using user-defined passbands, general filters, or Butterworth filters
misdataReconstruct missing input and output data
nkshiftShift data sequences
idresampResample time-domain data by decimation or interpolation
resampleResample time-domain data by decimation or interpolation (requires Signal Processing Toolbox software)
getTrendData offset and trend information
chgFreqUnitChange frequency units of frequency-response data model
fdelDelete specified data from frequency response data (FRD) models
TrendInfoOffset and linear trend slope values for detrending data

Examples and How To

Preprocess Data Using Quick Start

Subtract mean values from data, and specify estimation and validation data.

Extract and Model Specific Data Segments

This example shows how to create a multi-experiment, time-domain data set by merging only the accurate data segments and ignoring the rest.

How to Detrend Data Using the App

Before you can perform this task, you must have regularly-sampled, steady-state time-domain data imported into the System Identification app.

How to Detrend Data at the Command Line

Before you can perform this task, you must have time-domain data as an iddata object.

Resampling Data Using the App

Use the System Identification app to resample time-domain data.

Resampling Data at the Command Line

Use resample to decimate and interpolate time-domain iddata objects.

How to Filter Data Using the App

The System Identification app lets you filter time-domain data using a fifth-order Butterworth filter by enhancing or selecting specific passbands.

How to Filter Data at the Command Line

Use idfilt to apply passband and other custom filters to a time-domain or a frequency-domain iddata object.

Concepts

Handling Missing Data and Outliers

Handling missing or erroneous data values.

Handling Offsets and Trends in Data

Removing and restoring constant offsets and linear trends in data signals.

Resampling Data

Decimating and interpolating (resampling) data.

Filtering Data

Deciding whether to filter data before model estimation and how to prefilter data.

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