出错 untitledwuyong (第 32 行) [aic, bic] = aicbic(arima(1,1,4),route_data);
时间: 2024-01-04 21:04:47 浏览: 28
这个错误可能是由于 arima 函数的参数有误导致的。根据 MATLAB 的文档,arima 函数的参数应该是一个长度为 3 的向量,分别表示 AR、差分和 MA 的阶数,例如 arima(1,1,4) 表示 ARIMA(1,1,4) 模型。因此,你可以尝试修改代码,将 arima 函数的参数修改为正确的阶数,例如 arima(1,1,4) 表示 ARIMA(1,1,4) 模型。
另外,如果你的数据集非常大,可能需要使用较新版本的 MATLAB 或者使用分布式计算来处理数据。
相关问题
优化下下面一段代码:lim=round(length(residuals)/10); if lim>=10 lim=10; end train_data=iddata(residuals); save_data=[]; for p=1:lim for q=1:lim num=armax(train_data,[p,q]); AIC=aic(num); save_data=[save_data:p q AIC]; reli_ts(p,q)=AIC; end end
可以尝试进行以下优化:
1. 将 lim 的计算和判断放在 iddata() 函数中,可以避免多次计算和判断。
2. 使用矢量化运算,避免使用嵌套循环。
3. 使用预分配数组,避免多次扩展数组。
优化后的代码如下:
lim = min(10, round(length(residuals) / 10));
train_data = iddata(residuals, [], []);
save_data = zeros(lim^2, 3);
reli_ts = zeros(lim);
[p, q] = meshgrid(1:lim);
pq = [p(:), q(:)];
for i = 1:size(pq, 1)
num = armax(train_data, pq(i,:));
AIC = aic(num);
save_data(i,:) = [pq(i,:), AIC];
reli_ts(pq(i,1), pq(i,2)) = AIC;
end
注:优化后的代码未经测试,如有错误请谅解。
python ARIMA aic bic
ARIMA (Autoregressive Integrated Moving Average) model is a popular time series forecasting model that combines autoregressive (AR), differencing (I), and moving average (MA) components. The AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) are two common criteria used for model selection in ARIMA.
AIC and BIC are both measures of the goodness-of-fit of a statistical model. They take into account both the model's performance and the complexity of the model. The lower the value of AIC or BIC, the better the model is considered to be.
In ARIMA modeling, AIC and BIC can be used to compare different ARIMA models and select the one that provides the best fit to the data. These criteria help in avoiding overfitting by penalizing complex models that may not improve the forecasting performance significantly.
To calculate AIC and BIC for an ARIMA model, you can use the following formulas:
AIC = -2 * log-likelihood + 2 * p
BIC = -2 * log-likelihood + log(n) * p
where log-likelihood is the log-likelihood function value of the model, p is the number of parameters in the model, and n is the number of observations in the time series data.
In Python, you can obtain AIC and BIC values for an ARIMA model using the `statsmodels` library. After fitting an ARIMA model to your data, you can use the `aic` and `bic` attributes of the fitted model to access these values.
Here's an example code snippet:
```python
import statsmodels.api as sm
# Fit ARIMA model to your time series data
model = sm.tsa.ARIMA(data, order=(p, d, q))
results = model.fit()
# Get AIC and BIC values
aic = results.aic
bic = results.bic
```
Replace `data` with your actual time series data, and `p`, `d`, `q` with the order parameters of your ARIMA model.
Remember, AIC and BIC are just two of many possible criteria for model selection. It's always recommended to consider other factors such as forecasting accuracy, interpretability, and practical applicability of the model in addition to AIC and BIC.
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