python - 无法使用新观察更新 StatsModels SARIMAX (ValueError)

Question

我正在尝试使用 SciKitLearn 的 TimeSeriesSplit() 对时间序列数据集运行样本外验证，以创建训练/测试折叠。

想法是在训练折叠上训练 Statsmodel 的 SARIMAX，然后在不重新拟合模型的情况下在测试折叠上进行验证。为此，我们必须在预测之前，一次一个地迭代地将来自测试折叠的新观察结果附加到模型中。

但是，我在该追加步骤中收到 ValueError:ValueError:给定的 endog 没有扩展模型索引的索引。

这对我来说毫无意义。如果我为每个折叠打印出 print(max(train_fold.index), min(test_fold.index)) ，显然火车折叠的最后一个索引低于测试折叠的第一个索引。就我而言:

1983-05 1983-06
1984-05 1984-06
1985-05 1985-06
1986-05 1986-06
1987-05 1987-06

这是目前的完整代码。我确定我在做一些愚蠢的事情，但我被卡住了:

# Create a generator that yields the indices of our train and test folds
split = TimeSeriesSplit(n_splits=5).split(train_series)

# Loop through each fold
for train_idcs, test_idcs in split:

    # Create an empty prediction list to append to
    predictions = []

    # Create the folds
    train_fold = train_series[train_idcs]
    test_fold = train_series[test_idcs]

    # Fit the model on the training fold
    model_instance = sm.tsa.statespace.SARIMAX(
        train_fold,
        order=(1, 0, 0),
        seasonal_order=(1, 0, 0, 12),
        simple_differencing=True,
        enforce_stationarity=False,
        enforce_invertibility=False,
    )
    model_fitted = model_instance.fit(disp=False)

    # Create the initial prediction
    pred = model_fitted.forecast(steps=1)[
        0
    ]  # Slice so we just get the forecast value only
    predictions.append(pred)

    # Now loop through the test set, adding observations individually,
    # and getting the next prediction
    for i in range(len(test_fold)):

        # Get the next row
        next_row = test_fold.iloc[
            i : i + 1
        ]  # Returns single row but in series form (which statsmodels expects)

        # Append the row to the model
        model_fitted.append(next_row, refit=False)

        # Get the new prediction
        pred = model_fitted.forecast(steps=1)[
            0
        ]  # Slice so we just get the forecast value only
        predictions.append(pred)

    print(predictions)

model_fitted.append(next_row, refit=False) 是失败点。有任何想法吗？谢谢!

Answer 1

明白了!这很愚蠢。

SARIMAX 模型的 .append() 方法返回模型本身，而不是更改模型中存储的数据。

所以正确的代码很简单:model_fitted = model_fitted.append(next_row, refit=False)