循环 FBProphet 对每个区域进行建模会产生异常错误

Posted 2023-03-12

【中文标题】循环 FBProphet 对每个区域进行建模会产生异常错误

【英文标题】：Looping over FBProphet to model each region gives an exception error

【发布时间】：2021-02-12 05:44:45

【问题描述】：

我有一个多元时间序列数据，我正在使用 FBProhpet。为了可复制性，这里是一个模拟：

df1 = pd.DataFrame(
    pd.date_range(start="2020-01-01", end="2020-01-30", freq="D", name="ds")
)
df1["y"] = range(1, 31)
df1["region"] = "A"
df1["val1"] = range(1, 31)
df1["val2"] = np.sqrt(df["val1"]) * 2
df1["val3"] = (np.sqrt(df["val1"]) ** 3) + df["val2"]

df2 = pd.DataFrame(
    pd.date_range(start="2020-01-01", end="2020-01-30", freq="D", name="ds")
)
df2["y"] = range(1, 31)
df2["y"] = df2["y"] ** 2
df2["region"] = "B"
df2["val1"] = range(1, 31)
df2["val1"] = df2["val1"] ** 3
df2["val2"] = np.sqrt(df["val1"])
df2["val3"] = (np.sqrt(df["val1"]) ** 2) + df["val2"]

df3 = pd.DataFrame(
    pd.date_range(start="2020-01-01", end="2020-01-30", freq="D", name="ds")
)
df3["y"] = range(1, 31)
df3["y"] = np.sqrt(df2["y"])
df3["region"] = "C"
df3["val1"] = range(1, 31)
df3["val1"] = df3["y"] ** 3
df3["val2"] = np.sqrt(df3["y"])
df3["val3"] = (np.sqrt(df3["val1"]) ** 2) + df3["val2"]

df4 = df.append(df2)
df5 = df4.append(df3)

这里的目标是对每个区域的多元时间序列数据进行建模，并将模型保存在列表（或字典）中，稍后将调用该列表来预测隐藏的验证数据集。

我的代码如下：

p = Prophet()
p.add_regressor("val1")
p.add_regressor("val2")
p.add_regressor("val3")
models = []
for region in df5.region.unique():
    print(region)
    model = p.fit(train)
    models.append(model)

但是，我收到此错误：

---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
<ipython-input-265-48d06ef12449> in <module>
      6 for region in df5.region.unique():
      7     print(region)
----> 8     model = p.fit(train)
      9     models.append(model)

Exception: Prophet object can only be fit once. Instantiate a new object.

【参考方案1】：

将模型实例化移动到 for 循环中：

models = []
for region in df5.region.unique():
    p = Prophet()
    p.add_regressor("val1")
    p.add_regressor("val2")
    p.add_regressor("val3")

    print(region)
    model = p.fit(train)
    models.append(model)