User`s guide

Ways to Process Data for System Identification
Plot and analyze data
You can analyze your d ata by doing either of the following:
• Plotting data to examine both time- and frequency-domain behavior.
See “Analyzing Data Quality Using Plots” on page 1-75.
• Using the
advice commandtoanalyzethedataforthepresenceofconstant
offsets and trends, delay, feedback, and signal excitation levels.
See “Getting Advice About Your Data” on page 1-84.
Preprocess data
Review the data characteristics for any of the follow ing features to determine
if there is a need for preprocessing:
• Miss in g or f au lty values (also known as outliers). For example, you might
see gaps that indicate missing data, v alues that do not fitwiththerestof
the data, or noninformative values.
See “Handling Missing Data and Outliers” on page 1-90.
• Offse ts and drifts in sign al le vels (lo w-frequency disturbances).
See “Subtracting Trends from Signals (Detrending)” o n p age 1-94 for
information about subtracting means and linear trends, and “Filtering
Data” on page 1-107 for information about filtering.
• High-frequency disturbances above the frequency interval of interest for
the system dynamics.
See “Resampling Data” on page 1-100 for information about decimating and
interpolating values, and “Filtering Data” on page 1-107 for information
about filtering.
Select a subset of your data
You can use d ata s ele ction as a way to clean the data and exclude parts
with noisy or missing information. You can also use data selection to create
independent data sets for es timation and validation.
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