predict函数的用法？？？？？
哪位高手给详细说一下建立了ARMA模型之后，怎样用predict函数进行预测啊？就是我用100个数据进行建模，怎样用predict预测出101--110个数据啊？= predict(m,data,k,'InitialState',init)
里面的参数都应该怎么设啊？ :@( 没人帮忙吗？ Description
data is the output-input data as an iddata object, and m is any idmodel object (idpoly, idproc, idss, idgrey, or idarx). predict is meaningful only for time-domain data.
The argument k indicates that the k step-ahead prediction of y according to the model m is computed. In the calculation of yp(t), the model can use outputs up to time
and inputs up to the current time t. The default value of k is 1.
The output yp is an iddata object containing the predicted values as OutputData. The output argument mpred contains the k step-ahead predictor. This is given as a cell array, whose kth entry is an idpoly model for the predictor of output number k. Note that these predictor models have as input both input and output signals in the data set. The channel names indicate how the predictor model and the data fit together.
init determines how to deal with the initial state: init ='estimate': The initial state is set to a value that minimizes the norm of the prediction error associated with the model and the data. init = 'zero' sets the initial state to zero. init = 'model' uses the model's internally stored initial state. init = x0, where x0 is a column vector of appropriate dimension, uses that value as initial state.
If init is not specified, the model property m.InitialState is used, so that 'Estimate', 'Backcast', and 'Auto' set init = 'Estimate', while m.InitialState = 'Zero' sets init = 'zero', and 'Model' and 'Fixed' set init = 'model'.
An important use of predict is to evaluate a model's properties in the mid-frequency range. Simulation with sim (which conceptually corresponds to k = inf) can lead to levels that drift apart, since the low-frequency behavior is emphasized. One step-ahead prediction is not a powerful test of the model's properties, since the high-frequency behavior is stressed. The trivial predictorcan give good predictions in case the sampling of the data is fast.
Another important use of predict is to evaluate time-series models. The natural way of studying a time-series model's ability to reproduce observations is to compare its k step-ahead predictions with actual data.
Note that for output-error models, there is no difference between the k step-ahead predictions and the simulated output, since, by definition, output-error models only use past inputs to predict future outputs.
Algorithm
The model is evaluated in state-space form, and the state equations are simulated k steps ahead with initial value , whereis the Kalman filter state estimate.
Examples
Simulate a time series, estimate a model based on the first half of the data, and evaluate the four step-ahead predictions on the second half.
m0 = idpoly(,[],);
e = iddata([],randn(400,1));
y = sim(m0,e);
m = armax(y(1:200),);
yp = predict(m,y,4);
plot(y(201:400),yp(201:400))
Note that the last two commands are also achieved by
compare(y,m,4,201:400); {:{40}:}{:{33}:} 回复 3 # sogooda 的帖子
predict函数返回值为iddata对象，他不满足数学加减法，但是可以用plot绘制出值来！我现在对两个数据进行predict，然后想把预测结果加起来，怎么做？
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