我正在尝试实现一个时间序列模型并获得一些奇怪的异常，这些异常对我没有任何意义。我想知道我是犯了错误还是完全没有预料到的。这里有细节......
在训练我的模型时，我尝试进行网格搜索以找到最佳（p，d，q）设置。这是完整的代码（我将在下面解释这里发生的事情）：
下面的可重现代码基本上是来自https://machinelearningmastery.com/grid-search-arima-hyperparameters-with-python/的副本，稍有变化......：
import warnings
from pandas import Series
from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_squared_error
# evaluate an ARIMA model for a given order (p,d,q)
def evaluate_arima_model(X, arima_order):
# prepare training dataset
train_size = int(len(X) * 0.66)
train, test = X[0:train_size], X[train_size:]
history = [x for x in train]
# make predictions
predictions = list()
for t in range(len(test)):
model = ARIMA(history, order=arima_order)
model_fit = model.fit(disp=0)
yhat = model_fit.forecast()[0]
predictions.append(yhat)
history.append(test[t])
# calculate out of sample error
error = mean_squared_error(test, predictions)
return error
# evaluate combinations of p, d and q values for an ARIMA model
def evaluate_models(dataset, p_values, d_values, q_values):
dataset = dataset.astype('float64')
best_score, best_cfg = float("inf"), None
for p in p_values:
for d in d_values:
for q in q_values:
order = (p,d,q)
try:
print("Evaluating the settings: ", p, d, q)
mse = evaluate_arima_model(dataset, order)
if mse < best_score:
best_score, best_cfg = mse, order
print('ARIMA%s MSE=%.3f' % (order,mse))
except Exception as exception:
print("Exception occured...", type(exception).__name__, "\n", exception)
print('Best ARIMA%s MSE=%.3f' % (best_cfg, best_score))
# dataset
values = np.array([-1.45, -9.04, -3.64, -10.37, -1.36, -6.83, -6.01, -3.84, -9.92, -5.21,
-8.97, -6.19, -4.12, -11.03, -2.27, -4.07, -5.08, -4.57, -7.87, -2.80,
-4.29, -4.19, -3.76, -22.54, -5.87, -6.39, -4.19, -2.63, -8.70, -3.52,
-5.76, -1.41, -6.94, -12.95, -8.64, -7.21, -4.05, -3.01])
# evaluate parameters
p_values = [7, 8, 9, 10]
d_values = range(0, 3)
q_values = range(0, 3)
warnings.filterwarnings("ignore")
evaluate_models(values, p_values, d_values, q_values)
这是输出（不是一切，但它提供了足够的信息）：
Evaluating the settings: 7 0 0
Exception occured... LinAlgError
SVD did not converge
Evaluating the settings: 7 0 1
Exception occured... LinAlgError
SVD did not converge
Evaluating the settings: 7 0 2
Exception occured... ValueError
The computed initial AR coefficients are not stationary
You should induce stationarity, choose a different model order, or you can
pass your own start_params.
Evaluating the settings: 7 1 0
Exception occured... LinAlgError
SVD did not converge
Evaluating the settings: 7 1 1
Exception occured... ValueError
The computed initial AR coefficients are not stationary
You should induce stationarity, choose a different model order, or you can
pass your own start_params.
Evaluating the settings: 7 1 2
Exception occured... ValueError
The computed initial AR coefficients are not stationary
You should induce stationarity, choose a different model order, or you can
pass your own start_params.
Evaluating the settings: 7 2 0
Exception occured... LinAlgError
SVD did not converge
Evaluating the settings: 7 2 1
Exception occured... ValueError
The computed initial AR coefficients are not stationary
You should induce stationarity, choose a different model order, or you can
pass your own start_params.
Evaluating the settings: 7 2 2
Exception occured... ValueError
The computed initial AR coefficients are not stationary
You should induce stationarity, choose a different model order, or you can
pass your own start_params.
代码只是尝试所有不同的给定设置，训练模型，计算每个给定设置的MSE（均方误差），然后选择最佳设置（基于最小MSE）。
但是在训练过程中，代码不断抛出LinAlgError和ValueError异常，这对我没有任何意义。
并且据我所知，当抛出这些异常时，代码并不是真正训练某些设置，然后只是跳转到将要尝试的下一个设置。
为什么我会看到这些例外情况？他们会被忽视吗？我需要做些什么来解决它？
首先，回答你的具体问题：我认为“SVD没有收敛”是Statsmodels的ARIMA模型中的一个错误。这些天SARIMAX模型得到了更好的支持（并完成了ARIMA模型所做的所有工作+更多），所以我建议使用它。为此，请使用以下命令替换模型创建：
model = sm.tsa.SARIMAX(history, trend='c', order=arima_order, enforce_stationarity=False, enforce_invertibility=False)
话虽如此，我认为考虑到你的时间序列和你正在尝试的规范，你仍然不可能取得好成绩。
特别是，您的时间序列非常短，您只考虑极长的自回归滞后长度（p> 6）。用很少的数据点来估计很多参数是很困难的，特别是当你也有积分（d = 1或d = 2）和你也加入移动平均分量时。我建议您重新评估您正在考虑的模型。