测试 arima 模型 [英] test arima model

问题描述

我正在验证 Arima 模型，我想知道我的测试的临界值，以根据 p 值拒绝零假设.如果想要 95% 的置信度.这是我的临界值.

I am validating Arima models and I would like to know the critical value of my test to reject the null hypthesis depending on the p-value. If a want a confidence of 95%. which is my critical value.

1-pchisq(-2*(try2$loglik-try1$loglik),1)
 0.1817151
1-pchisq(-2*(try3$loglik-try2$loglik),1)
1

其中 try1、try2 和 try3 是三种不同的模型.谢谢！

Where try1, try2 and try3 are three different models. Thanks!

推荐答案

ARIMA(自回归综合移动平均)时间序列的交叉验证: K 折交叉验证不适用于时间序列.相反，使用回测技术，例如 walk-forward 和 滚动窗口.

Cross validation for ARIMA (AutoRegressive Integrated Moving Average) time series: K-fold cross validation does not work for time-series. Instead, use backtesting techniques like walk-forward and rolling windows.

自回归的 K 折交叉验证: 尽管交叉验证(通常)对时间序列 (ARIMA) 模型无效，但 K 折适用于自回归，只要所考虑的模型具有不相关的错误，并且您已经使用 Ljung Box Test，用于时间序列用例中的 XAI(可解释人工智能).

K-fold cross-validation for autoregression: Although cross-validation is (usually) not valid for time series (ARIMA) models, K-fold works for autoregressions as long as the models considered have uncorrelated errors, and you have tested it with the Ljung Box Test, for XAI (Explainable Artificial Intelligence) in time series use cases.

• 有一些 Python 统计库可以使用这些方法，这里有两个:Python 统计测试 和 Python 统计模型.

你总是可以很容易地研究 Python API 并重构为 R.

You can always study the Python APIs and refactor into R quite easily.

要获取值的差异，您可以简单地使用 Python 3.6+ PEP 487 描述符，您可以在其中强制执行始终返回 int8 的类型列表，以加快计算速度(列表:列表 -> 整数列表):

To get the diff of values, you can simply enforce int8's using Python 3.6+ PEP 487 Descriptors, where you can enforce a type list that always returns int8's, for faster computation as well (list : list -> list of ints):

list_a = [1,2,3]
list_b = [2,3]
print(set(list_a).difference(set(list_b)))

答案是set([1])

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