데이터 전처리하기

평균, 오프셋 및 선형 추세의 제거, 누락된 데이터 재구성, 데이터 샘플링 레이트 변경

함수

`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`	iddata 객체에서의 차이 신호
`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
`idresampOptions`	Option set for `idresamp` (R2023a 이후)
`resample`	(Not recommended) Resample time-domain data that is stored in an `iddata` object 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.
특정 데이터 세그먼트 추출 및 모델링하기
이 예제에서는 정확한 데이터 세그먼트만 병합하고 나머지는 무시하여 다중 실험, 시간 영역 데이터 세트를 만드는 방법을 보여줍니다.
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
Decimate and interpolate time-domain data.
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.