Traceback (most recent call last): File "d:\Users\lenovo\anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 3629, in get_loc return self._engine.get_loc(casted_key) File "pandas\_libs\index.pyx", line 136, in pandas._libs.index.IndexEngine.get_loc File "pandas\_libs\index.pyx", line 163, in pandas._libs.index.IndexEngine.get_loc File "pandas\_libs\hashtable_class_helper.pxi", line 5198, in pandas._libs.hashtable.PyObjectHashTable.get_item File "pandas\_libs\hashtable_class_helper.pxi", line 5206, in pandas._libs.hashtable.PyObjectHashTable.get_item KeyError: ('User_id', 'Coupon_id', 'max_date_received', 'min_date_received') The above exception was the direct cause of the following exception: Traceback (most recent call last): File "D:\python相关2\任务二.py", line 251, in <module> train = get_dataset(train_history_field, train_middle_field, train_label_field) File "D:\python相关2\任务二.py", line 147, in get_dataset user_feat = get_user_feat(label_field) # 用户特征 File "D:\python相关2\任务二.py", line 69, in get_user_feat t2=t2['User_id','Coupon_id','max_date_received','min_date_received'] File "d:\Users\lenovo\anaconda3\lib\site-packages\pandas\core\frame.py", line 3505, in __getitem__ indexer = self.columns.get_loc(key) File "d:\Users\lenovo\anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 3631, in get_loc raise KeyError(key) from err KeyError: ('User_id', 'Coupon_id', 'max_date_received', 'min_date_received')

这段代码运行出错了，错误提示是 KeyError，意思是字典或者 DataFrame 中没有这个 key。具体原因是在 get_user_feat 函数中调用了 t2['User_id','Coupon_id','max_date_received','min_date_received']，但是 DataFrame 对象 t2 中没有这个 key。这个错误可能是因为在调用 t2['User_id','Coupon_id','max_date_received','min_date_received'] 时，需要将 key 用中括号括起来，即 t2[['User_id','Coupon_id','max_date_received','min_date_received']]。

相关问题

Traceback (most recent call last): File "/Users/yaomanlin/opt/Anaconda/anaconda3/bin/lib/python3.8/site-packages/pandas/core/indexes/base.py", line 3803, in get_loc return self._engine.get_loc(casted_key)

这是一个 Python 的错误信息，它发生在 Pandas 库的基础索引模块中。具体来说，这里是在 get_loc() 函数中出错了。get_loc() 的作用是根据传入的键值查找索引中对应的位置。这个错误通常是因为传入的键值不在索引中而导致的。可能需要检查一下传入的键值是否正确，或者尝试使用其它的查找方法。如果需要更详细的信息，可以查看完整的错误信息。

Traceback (most recent call last): File "E:\anaconda\lib\site-packages\pandas\core\indexes\range.py", line 385, in get_loc return self._range.index(new_key) ValueError: 0 is not in range The above exception was the direct cause of the following exception: Traceback (most recent call last): File "F:\pythonproject\ARIMA-GRACH\6.py", line 34, in <module> arima_pred = arima_result.forecast()[0][0] File "E:\anaconda\lib\site-packages\pandas\core\series.py", line 958, in __getitem__ return self._get_value(key) File "E:\anaconda\lib\site-packages\pandas\core\series.py", line 1069, in _get_value loc = self.index.get_loc(label) File "E:\anaconda\lib\site-packages\pandas\core\indexes\range.py", line 387, in get_loc raise KeyError(key) from err KeyError: 0

这个错误是由于 `test_ts` 的索引从0开始，而 `pd.Series([test_ts[i]], index=[test_ts.index[i]-1])` 中的 `index` 参数是从-1开始的，导致索引对不上。你可以将 `index` 参数修改为从0开始的，如下所示：

arima_result = arima_result.append(pd.Series([test_ts[i]], index=[test_ts.index[i]]))
garch_result = garch_result.append(pd.Series([arima_result.resid[-1]], index=[test_ts.index[i]]))

另外，如果你想要使用 ARIMA-GARCH 模型对时间序列进行预测，你还需要实现 GARCH 模型部分的代码。具体实现方式可以参考下面的代码：

from arch import arch_model

# 训练 GARCH 模型
garch_model = arch_model(train_ts, p=1, q=1)
garch_result = garch_model.fit(update_freq=5).forecast(horizon=len(test_ts), reindex=False)

arima_model = ARIMA(train_ts, order=(1, 1, 1))
arima_result = arima_model.fit()
pred_ts = []
for i in range(len(test_ts)):
    # 预测ARIMA模型的下一个值
    arima_pred = arima_result.forecast()[0][0]
    # 使用GARCH模型计算方差
    var = garch_result.forecast(horizon=1).variance.iloc[-1, 0]
    # 计算标准差
    std = np.sqrt(var)
    # 计算置信区间
    conf_int = (arima_pred - 1.96 * std, arima_pred + 1.96 * std)
    # 将预测结果添加到列表中
    pred_ts.append(arima_pred)
    # 将预测结果添加到模型中
    arima_result = arima_result.append(pd.Series([test_ts[i]], index=[test_ts.index[i]]))
    garch_result = garch_result.append(pd.Series([arima_result.resid[-1]], index=[test_ts.index[i]]))