detrend | Subtract offset or trend from time-domain signals contained in
iddata objects |
retrend | Add offsets or trends to time-domain data signals stored in iddata
objects |
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 | Create trend information object to store offset, mean, and trend information for
time-domain signals stored in iddata object |
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 |
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.
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.
Decimating and interpolating (resampling) data.
Deciding whether to filter data before model estimation and how to prefilter data.