The establishment of matlab autoregressive model

daizhenbin168 注册会员
2023-02-27 19:14

the reference answer GPT ᴼ ᴾ ᴱ ᴺ ᴬ ᴵ < br / > to case western reserve university bearing fault data imported to MATLAB, and establish the AR model to predict, can according to the following steps:

1. Import data
Use the load command in MATLAB to load the.mat file and import the data into MATLAB. For example, assuming the data is stored in a data.mat file, you can import the data using the following command:


2, data preprocessing
Necessary data preprocessing operations, such as removing trend, stabilizing, etc., in order to better establish AR model. Functions such as detrend, diff and zscore in MATLAB can be used for data preprocessing.

Establish AR model
Use ar command in MATLAB to establish AR model, the command syntax is:
mdl = ar(data, p);

Where, data is the input data sequence, and p is the order of AR model. The function will return an AR model object mdl, through which the parameters, residual sequence, prediction and other information of the AR model can be accessed.

4. Model order
The arorder command in MATLAB can be used to determine the order of AR model. The syntax of this command is:

p = arorder(data, maxp);

Where, data is the input data sequence, and maxp is the maximum order of AR model. Function will return a order p, representing the optimal order of the established AR model.

5. Model prediction
The predict command in MATLAB can be used to predict the AR model. The syntax of this command is:

[ypred, yci] = predict(mdl, data, numpred);

Where, mdl is the AR model object, data is the input data sequence, and numpred is the number of data points to be predicted. The function returns the predicted value ypred and the predicted confidence interval yci.

The above are the general steps of establishing AR model for prediction in MATLAB. If you need more detailed operations, you can refer to the relevant documentation and sample programs in MATLAB.

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2023-02-27 19:14
Update Time
2023-02-27 19:14

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