You can represent data in the format of this toolbox by doing one of the following:

• For working in the GUI, import data into the System Identification Tool GUI.

  See Importing Data into the GUI.

• For working at the command line, create an iddata or idfrd object.

  For time-domain or frequency-domain data, see Representing Time- and Frequency-Domain Data Using iddata Objects.

  For frequency-response data, see Representing Frequency-Response Data Using idfrd Objects.

• To simulate data with and without noise, see Generating Data Using Simulation.

You can analyze your data by doing either of the following:

• Plotting data to examine both time- and frequency-domain behavior.

  See Analyzing Data Quality.

• Using the advice command to analyze the data for the presence of constant offsets and trends, delay, possible feedback, and signal excitation levels.

  See How to Analyze Data Using the advice Command.

Review the data characteristics for any of the following features to determine if there is a need for preprocessing:

• Missing or faulty values (also known as outliers). For example, you might see gaps that indicate missing data, values that do not fit with the rest of the data, or noninformative values.

  See Handling Missing Data and Outliers.

• Offsets and drifts in signal levels (low-frequency disturbances).

  See Handling Offsets and Trends in Data for information about subtracting means and linear trends, and Filtering Data for information about filtering.

• High-frequency disturbances above the frequency interval of interest for the system dynamics.

  See Resampling Data for information about decimating and interpolating values, and Filtering Data for information about filtering.

You can use data selection 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 estimation and validation.

To learn more about selecting data, see Selecting Subsets of Data.

You can combine data from several experiments into a single data set. The model you estimate from a data set containing several experiments describes the average system that represents these experiments.

To learn more about creating multiple-experiment data sets, see Creating Multiexperiment Data Sets in the GUI or Creating Multiexperiment Data at the Command Line.