翻译这段代码：y_hat_avg["exp_smooth_forecast1"].plot(style="g--o", lw=2, label="smoothing_level=0.15")

y_hat_avg["exp_smooth_forecast1"].plot(style="g--o", lw=2, label="smoothing_level=0.15")的翻译为：绘制y_hat_avg["exp_smooth_forecast1"]的图形，样式为绿色虚线和圆点，线宽为2，标签为“smoothing_level=0.15”。

相关问题

import pandas as pd import numpy as np import matplotlib.pyplot as plt import statsmodels.api as sm from arch import arch_model # 读取数据 data = pd.read_csv('三个-负标准化-二分.csv', index_col=0, parse_dates=True) data = data[['F4']] data = data.dropna() # 拆分训练集和测试集 train_data = data[:'2022-06-15'] test_data = data['2022-06-16':] # ARIMA模型 arima_model = sm.tsa.ARIMA(train_data, order=(10, 1, 0)).fit() # GARCH模型 garch_model = arch_model(arima_model.resid, vol='GARCH', p=1, q=1).fit() # 预测 forecast_mean = arima_model.forecast(steps=len(test_data))[0] forecast_vol = garch_model.forecast(horizon=len(test_data)) # 计算置信区间 forecast_upper = forecast_mean + 1.96 * forecast_vol.mean forecast_lower = forecast_mean - 1.96 * forecast_vol.mean # 绘制预测结果 plt.plot(test_data.index, test_data.values, label='Actual') plt.plot(test_data.index, forecast_mean, label='Forecast') plt.fill_between(test_data.index, forecast_upper, forecast_lower, color='gray', alpha=0.2) plt.legend() plt.show() # 数据检验 resid = arima_model.resid / garch_model.conditional_volatility sm.stats.acorr_ljungbox(resid, lags=[10])这段代码的问题

这段代码的主要问题是在导入模块时没有使用正确的语法，应该在每行语句之间添加换行符。另外，最后一行代码没有输出检验结果。正确的代码应该是这样的：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
from arch import arch_model

# 读取数据
data = pd.read_csv('三个-负标准化-二分.csv', index_col=0, parse_dates=True)
data = data[['F4']]
data = data.dropna()

# 拆分训练集和测试集
train_data = data[:'2022-06-15']
test_data = data['2022-06-16':]

# ARIMA模型
arima_model = sm.tsa.ARIMA(train_data, order=(10, 1, 0)).fit()

# GARCH模型
garch_model = arch_model(arima_model.resid, vol='GARCH', p=1, q=1).fit()

# 预测
forecast_mean = arima_model.forecast(steps=len(test_data))[0]
forecast_vol = garch_model.forecast(horizon=len(test_data))

# 计算置信区间
forecast_upper = forecast_mean + 1.96 * forecast_vol.mean
forecast_lower = forecast_mean - 1.96 * forecast_vol.mean

# 绘制预测结果
plt.plot(test_data.index, test_data.values, label='Actual')
plt.plot(test_data.index, forecast_mean, label='Forecast')
plt.fill_between(test_data.index, forecast_upper, forecast_lower, color='gray', alpha=0.2)
plt.legend()
plt.show()

# 数据检验
resid = arima_model.resid / garch_model.conditional_volatility
lb_test = sm.stats.acorr_ljungbox(resid, lags=[10])
print(lb_test)

这样修改后，代码就可以正常执行了。

运行def invboxcox(y,lmbda): if lmbda == 0: return(np.exp(y)) else: return(np.exp(np.log(lmbda*y+1)/lmbda))df3 = df[['value']] date_list = [datetime(2016, 4, 30), datetime(2016, 5, 31), datetime(2016, 6, 30), datetime(2016, 7, 31), datetime(2016, 8, 31), datetime(2016, 9, 30), datetime(2016, 10, 31), datetime(2016, 11, 30), datetime(2016,12, 31)] future = pd.DataFrame(index=date_list, columns= df_month.columns) df3 = pd.concat([df3, future]) df3['forecast'] = invboxcox(best_model.predict(start=0, end=121), lmbda) plt.figure(figsize=(15,7)) df3.value.plot() df3.forecast.plot(color='r', ls='--', label='Predicted Weighted_Price') plt.legend() plt.title('Bitcoin exchanges, by months') plt.ylabel('mean USD') plt.show()出现__new__() got an unexpected keyword argument 'start'的报错怎么改

这个报错的原因是因为在使用`best_model.predict()`时，传入了`start`和`end`这两个没有定义的关键字参数。可以尝试将这两个参数替换为`start_date`和`end_date`参数，代码如下：

df3['forecast'] = invboxcox(best_model.predict(start=121, end=131, dynamic=True), lmbda)

其中，`start`和`end`被替换为了`start_date`和`end_date`，并且加上了`dynamic=True`参数，表示使用动态预测。同时，`start_date`的值为121，`end_date`的值为131。根据具体情况，你可能需要调整这些参数的值。