Sarimax从样品预测中使用外源数据

Question

我正在与Sarimax进行时间分析，并一直在努力。

我认为我已经成功地拟合了一个模型，并用它来做出预测。但是，我不知道如何通过外源数据从样本预测中制定出来。

我可能会做错了整个事情，所以我在下面包含了一些示例数据；

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from pandas import datetime
import statsmodels.api as sm

# Defining Sample data
df = pd.DataFrame({'date':['2019-01-01','2019-01-02','2019-01-03',
                         '2019-01-04','2019-01-05','2019-01-06',
                         '2019-01-07','2019-01-08','2019-01-09',
                         '2019-01-10','2019-01-11','2019-01-12'],
                  'price':[78,60,62,64,66,68,70,72,74,76,78,80],
                 'factor1':[178,287,152,294,155,245,168,276,165,275,178,221]
                })
# Changing index to datetime
df['date'] = pd.to_datetime(df['date'], errors='ignore', format='%Y%m%d')
select_dates = df.set_index(['date'])

df = df.set_index('date')
df.index = pd.to_datetime(df.index)
df.sort_index(inplace=True)
df.dropna(inplace=True)

# Splitting Data into test and training sets manually
train = df.loc['2019-01-01':'2019-01-09']
test = df.loc['2019-01-10':'2019-01-12']

# setting index to datetime for test and train datasets
train.index = pd.DatetimeIndex(train.index).to_period('D')
test.index = pd.DatetimeIndex(test.index).to_period('D')

# Defining and fitting the model with training data for endogenous and exogenous data

model=sm.tsa.statespace.SARIMAX(train['price'],
                                order=(0, 0, 0),
                                seasonal_order=(0, 0, 0,12), 
                                exog=train.iloc[:,1:],
                                time_varying_regression=True,
                                mle_regression=False)
model_1= model.fit(disp=False)

# Defining exogenous data for testing 
exog_test=test.iloc[:,1:]

# Forecasting out of sample data with exogenous data
forecast = model_1.forecast(3, exog=exog_test)

所以我的问题确实在最后一行，如果我想要超过3个步骤，该怎么办？

Answer 1

我会尝试回答这个问题，因为它主要与统计模型软件包的数据类型和文档有关。

根据文档，“步骤”是一个整数，从示例末尾预测的步骤 。这也意味着，如果您有兴趣获得三个以上的步骤，则需要提供其他数组数据进行培训和测试数据（请注意 - 两者）。
（）
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这是我在将步长增加一个时会遇到的两个错误

/dev/generated/statsmodels.tsa.statespace.sarimax.sarimax.sarimaxresults.forecast.html 尺寸3成形（4,1）
提供的外源值不是适当的形状。需要（4，1），得到（3，1）。

valueerror ：外源变量中的行数与您要求它预测的时间段的数量不匹配，

因为这就是所述扩展的测试集可以很好地工作，并在这里为您提供其他预测。有效的代码和工作笔记本链接：

import pandas as pd
import numpy as np
# from sklearn.model_selection import train_test_split 
# why import this if you want to do tran/test manually? 
from pandas import datetime

# Defining Sample data
df=pd.DataFrame({'date':['2019-01-01','2019-01-02','2019-01-03',
                         '2019-01-04','2019-01-05','2019-01-06',
                         '2019-01-07','2019-01-08','2019-01-09',
                         '2019-01-10','2019-01-11','2019-01-12'],
                  'price':[78,60,62,64,66,68,70,72,74,76,78,80],
                 'factor1':[178,287,152,294,155,245,168,276,165,275,178,221]
                })
# Changing index to datetime
df['date'] = pd.to_datetime(df['date'], errors='ignore', format='%Y%m%d')
select_dates = df.set_index(['date'])

df = df.set_index('date')
df.index = pd.to_datetime(df.index)
df.sort_index(inplace=True)
df.dropna(inplace=True)

# Splitting Data into test and training sets manually
train=df.loc['2019-01-01':'2019-01-09']
# I made a change here #CHANGED 10 to 09 so one more month got added
# that means my input array is now 4,1 (if you add a column array is - ) 
# (4,2) 
# I can give any step from -4,0,4 (integral)

test=df.loc['2019-01-09':'2019-01-12']

# setting index to datetime for test and train datasets
train.index = pd.DatetimeIndex(train.index).to_period('D')
test.index = pd.DatetimeIndex(test.index).to_period('D')

# Defining and fitting the model with training data for endogenous and exogenous data
import statsmodels.api as sm

model=sm.tsa.statespace.SARIMAX(train['price'],
                                order=(0, 0, 0),
                                seasonal_order=(0, 0, 0,12), 
                                exog=train.iloc[:,1:],
                                time_varying_regression=True,
                                mle_regression=False)
model_1= model.fit(disp=False)

# Defining exogenous data for testing 
exog_test=test.iloc[:,1:]

# Forcasting out of sample data with exogenous data
forecast = model_1.forecast(4, exog=exog_test)