Preprocess Data

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

Functions

`detrend`	Subtract offset or trend from time-domain signals contained in`iddata`对象
`retrend`	Add offsets or trends to time-domain data signals stored in`iddata`对象
`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 (requiresSignal Processing Toolboxsoftware)
`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

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

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

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

Before you can perform this task, you must have time-domain data as aniddataobject.

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

Useresampleto decimate and interpolate time-domainiddata对象.

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

Useidfiltto apply passband and other custom filters to a time-domain or a frequency-domainiddataobject.

Handling missing or erroneous data values.

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.