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Preprocess Data

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


detrend Subtract offset or trend from data signals
retrend Add offsets or trends to data signals
diff Difference signals in iddata objects
idfilt Filter data using user-defined passbands, general filters, or Butterworth filters
misdata Reconstruct missing input and output data
nkshift Shift data sequences
idresamp Resample time-domain data by decimation or interpolation
resample Resample time-domain data by decimation or interpolation (requires Signal Processing Toolbox software)
getTrend Data offset and trend information
chgFreqUnit Change frequency units of frequency-response data model
fdel Delete specified data from frequency response data (FRD) models
TrendInfo Offset 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.


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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