解释exog=test_data.iloc[:, 1:]
时间: 2024-01-13 17:05:50 浏览: 37
这是一行Python代码,其中 test_data 是一个 Pandas DataFrame 对象,iloc 是 Pandas 中用于通过位置索引选取数据的函数。冒号前面的空处表示选取所有行,而冒号后面的数字 1 表示选取从第二列(索引为 1)开始的所有列。因此,exog=test_data.iloc[:, 1:] 的作用是将 test_data 中除了第一列(通常为目标变量)之外的所有列作为自变量(即外生变量)赋值给 exog 变量。这通常用于机器学习模型的训练和预测。
相关问题
import itertools import warnings import pandas as pd import numpy as np import statsmodels.api as sm from datetime import datetime from statsmodels.tsa.arima.model import ARIMA from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from statsmodels.stats.diagnostic import acorr_ljungbox from sklearn.model_selection import train_test_split data = pd.read_csv('data.csv', parse_dates=['x'], index_col='x') train_data1, test_data = train_test_split(data1, test_size=0.3, shuffle=False) data['lag1'] = data['y'].shift(1) data['lag2'] = data['y'].shift(2) data['lag3'] = data['y'].shift(3) data['lag4'] = data['y'].shift(4) data['lag5'] = data['y'].shift(5) data['lag6'] = data['y'].shift(6) data['lag7'] = data['y'].shift(7) data.dropna(inplace=True) train_data, test_data1 = train_test_split(data, test_size=0.3, shuffle=False) g=int(input("输入P的峰值: ")) h=int(input("输入D的峰值: ")) i=int(input("输入Q的峰值: ")) p = range(0, g) d = range(0, h) q = range(0, i) pdq = list(itertools.product(p, d, q)) best_pdq = None best_aic = np.inf for param in pdq: model = sm.tsa.ARIMA(data['y'], exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']], order=param) results = model.fit() aic = results.aic if aic < best_aic: best_pdq = param best_aic = aic a=best_pdq[0] b=best_pdq[1] c=best_pdq[2] model = ARIMA(data['y'], exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']], order=(a,b,c)) results = model.fit() max_lag = model.k_ar model_fit = model.fit() resid = model_fit.resid lb_test = acorr_ljungbox(resid) p_value=round(lb_test['lb_pvalue'][max_lag],4) if p_value>0.05: forecast = results.forecast(steps=1, exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']].iloc[-1:]) # 输出预测值 forecast.index[0].strftime('%Y-%m') print("下个月的预测结果是",round(forecast[0])) else: print('输入的数据不适合使用arima模型进行预测分析,请尝试其他模型'),如何添加检测预测准确率的python代码
要添加检测预测准确率的代码,可以使用均方根误差(RMSE)或平均绝对误差(MAE)等指标。以RMSE为例,可以按以下步骤进行计算和输出:
1. 在导入所需的库后,将测试数据集中的实际值和预测值提取出来:
```
test_actual = test_data['y']
test_pred = results.predict(start=test_data.index[0], end=test_data.index[-1], exog=test_data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']])
```
2. 计算RMSE并输出结果:
```
rmse = np.sqrt(((test_pred - test_actual) ** 2).mean())
print('测试集的RMSE为', rmse)
```
完整的代码如下:
```
import itertools
import warnings
import pandas as pd
import numpy as np
import statsmodels.api as sm
from datetime import datetime
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.stats.diagnostic import acorr_ljungbox
from sklearn.model_selection import train_test_split
# 导入数据
data = pd.read_csv('data.csv', parse_dates=['x'], index_col='x')
# 划分训练集和测试集
train_data1, test_data = train_test_split(data1, test_size=0.3, shuffle=False)
# 添加滞后特征
data['lag1'] = data['y'].shift(1)
data['lag2'] = data['y'].shift(2)
data['lag3'] = data['y'].shift(3)
data['lag4'] = data['y'].shift(4)
data['lag5'] = data['y'].shift(5)
data['lag6'] = data['y'].shift(6)
data['lag7'] = data['y'].shift(7)
data.dropna(inplace=True)
# 再次划分训练集和测试集
train_data, test_data1 = train_test_split(data, test_size=0.3, shuffle=False)
# 寻找最优的ARIMA模型
g=int(input("输入P的峰值: "))
h=int(input("输入D的峰值: "))
i=int(input("输入Q的峰值: "))
p = range(0, g)
d = range(0, h)
q = range(0, i)
pdq = list(itertools.product(p, d, q))
best_pdq = None
best_aic = np.inf
for param in pdq:
model = sm.tsa.ARIMA(data['y'], exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']], order=param)
results = model.fit()
aic = results.aic
if aic < best_aic:
best_pdq = param
best_aic = aic
a=best_pdq[0]
b=best_pdq[1]
c=best_pdq[2]
model = ARIMA(data['y'], exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']], order=(a,b,c))
results = model.fit()
# 进行预测并计算测试集的RMSE
max_lag = model.k_ar
model_fit = model.fit()
resid = model_fit.resid
lb_test = acorr_ljungbox(resid)
p_value=round(lb_test['lb_pvalue'][max_lag],4)
if p_value>0.05:
# 预测结果
forecast = results.forecast(steps=1, exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']].iloc[-1:])
print("下个月的预测结果是",round(forecast[0]))
# 计算RMSE并输出结果
test_actual = test_data['y']
test_pred = results.predict(start=test_data.index[0], end=test_data.index[-1], exog=test_data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']])
rmse = np.sqrt(((test_pred - test_actual) ** 2).mean())
print('测试集的RMSE为', rmse)
else:
print('输入的数据不适合使用ARIMA模型进行预测分析,请尝试其他模型')
```
import itertools import warnings import pandas as pd import numpy as np import statsmodels.api as sm from datetime import datetime from statsmodels.tsa.arima.model import ARIMA from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from statsmodels.stats.diagnostic import acorr_ljungbox from sklearn.model_selection import train_test_split data = pd.read_csv('data.csv', parse_dates=['x'], index_col='x') train_data1, test_data = train_test_split(data1, test_size=0.3, shuffle=False) data['lag1'] = data['y'].shift(1) data['lag2'] = data['y'].shift(2) data['lag3'] = data['y'].shift(3) data['lag4'] = data['y'].shift(4) data['lag5'] = data['y'].shift(5) data['lag6'] = data['y'].shift(6) data['lag7'] = data['y'].shift(7) data.dropna(inplace=True) train_data, test_data1 = train_test_split(data, test_size=0.3, shuffle=False) g=int(input("输入P的峰值: ")) h=int(input("输入D的峰值: ")) i=int(input("输入Q的峰值: ")) p = range(0, g) d = range(0, h) q = range(0, i) pdq = list(itertools.product(p, d, q)) best_pdq = None best_aic = np.inf for param in pdq: model = sm.tsa.ARIMA(data['y'], exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']], order=param) results = model.fit() aic = results.aic if aic < best_aic: best_pdq = param best_aic = aic a=best_pdq[0] b=best_pdq[1] c=best_pdq[2] model = ARIMA(data['y'], exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']], order=(a,b,c)) results = model.fit() max_lag = model.k_ar model_fit = model.fit() resid = model_fit.resid lb_test = acorr_ljungbox(resid) p_value=round(lb_test['lb_pvalue'][max_lag],4) if p_value>0.05: forecast = results.forecast(steps=1, exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']].iloc[-1:]) forecast.index[0].strftime('%Y-%m') print("下个月的预测结果是",round(forecast[0])) def comput_acc(real,predict,level): num_error=0 for i in range(len(real)): if abs(real[i]-predict[i])/real[i]>level: num_error+=1 return 1-num_error/len(real) print("置信水平:{},预测准确率:{}".format(0.2,comput_acc(test_x,y_pred,0.2))) else: print('输入的数据不适合使用arima模型进行预测分析,请尝试其他模型')如何修改代码使其正常运行
在你的代码中,有一些变量名没有定义,例如`data1`和`test_x`,所以我假设这些是之前定义过的变量并且进行了更改。此外,需要加入一些库的引用,如`itertools`和`sklearn`。以下是修改后的代码:
```
import itertools
import warnings
import pandas as pd
import numpy as np
import statsmodels.api as sm
from datetime import datetime
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.stats.diagnostic import acorr_ljungbox
from sklearn.model_selection import train_test_split
data = pd.read_csv('data.csv', parse_dates=['x'], index_col='x')
train_data1, test_data = train_test_split(data, test_size=0.3, shuffle=False)
data['lag1'] = data['y'].shift(1)
data['lag2'] = data['y'].shift(2)
data['lag3'] = data['y'].shift(3)
data['lag4'] = data['y'].shift(4)
data['lag5'] = data['y'].shift(5)
data['lag6'] = data['y'].shift(6)
data['lag7'] = data['y'].shift(7)
data.dropna(inplace=True)
train_data, test_data1 = train_test_split(data, test_size=0.3, shuffle=False)
g=int(input("输入P的峰值: "))
h=int(input("输入D的峰值: "))
i=int(input("输入Q的峰值: "))
p = range(0, g)
d = range(0, h)
q = range(0, i)
pdq = list(itertools.product(p, d, q))
best_pdq = None
best_aic = np.inf
for param in pdq:
model = sm.tsa.ARIMA(data['y'], exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']], order=param)
results = model.fit()
aic = results.aic
if aic < best_aic:
best_pdq = param
best_aic = aic
a=best_pdq[0]
b=best_pdq[1]
c=best_pdq[2]
model = ARIMA(data['y'], exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']], order=(a,b,c))
results = model.fit()
max_lag = model.k_ar
model_fit = model.fit()
resid = model_fit.resid
lb_test = acorr_ljungbox(resid)
p_value=round(lb_test[1][max_lag],4)
if p_value>0.05:
forecast = results.forecast(steps=1, exog=data[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']].iloc[-1:])
forecast.index[0].strftime('%Y-%m')
print("下个月的预测结果是",round(forecast[0]))
def comput_acc(real,predict,level):
num_error=0
for i in range(len(real)):
if abs(real[i]-predict[i])/real[i]>level:
num_error+=1
return 1-num_error/len(real)
y_pred = results.predict(start=test_data1.index[0], end=test_data1.index[-1], exog=test_data1[['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'lag6', 'lag7']])
print("置信水平:{},预测准确率:{}".format(0.2,comput_acc(test_data1['y'],y_pred,0.2)))
else:
print('输入的数据不适合使用arima模型进行预测分析,请尝试其他模型')
```
需要注意的是,由于ARIMA模型需要对历史数据进行训练,因此在使用`predict`方法来进行预测时,需要指定预测的时间范围,这里已经加入了相应的代码。同时,在计算预测准确率时,需要使用测试集的真实值和预测值,因此需要将测试集的真实值作为参数传入`comput_acc`函数中。