Python\u分解函数中的残差图显示不正确

剩余绘图未在我的绘图中正确显示。我不明白这是什么问题。请帮我做这个。轴心有一些问题。我正在提取新冠病毒19的数据，并绘制一阶数据（固定集）。我已删除所有nan值

数据格式为 日期值 268 2020-10-16 745.0 269 2020-10-17 428.0 270 2020-10-18 465.0

ecomposition = seasonal_decompose(data_set_3, model='additive', period=7)

    trend = decomposition.trend
    seasonal = decomposition.seasonal
    residual = decomposition.resid

    plt.subplot(411)
    plt.plot(data_set_3, label='Original')
    plt.legend(loc='best')

    plt.subplot(412)
    plt.plot(trend, label='Trend')
    plt.legend(loc='best')

    plt.subplot(413)
    plt.plot(seasonal, label='Seasonality')
    plt.legend(loc='best')

    plt.subplot(414)
    plt.plot(residual, label='Residuals')
    plt.legend(loc='best')

    plt.tight_layout()

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将频率和周期添加到季节分解中以实现平滑。它会起作用的

from pandas_datareader import data as pdr
from statsmodels.graphics import tsaplots
import statsmodels.api as sm

current_date=datetime.datetime.now()
start_date=datetime.datetime(current_date.year,1,1)
df = pdr.get_data_yahoo("MSFT",start_date,current_date).reset_index()

decomposition=sm.tsa.seasonal_decompose(x=df['High'],model='additive',         extrapolate_trend='freq', period=30)
decomposition.plot()
plt.show()

decomposition_trend=decomposition.trend
ax= decomposition_trend.plot(figsize=(14,2))
ax.set_xlabel('Date')
ax.set_ylabel('Trend of time series')
ax.set_title('Trend values of the time series')
plt.show()

decomposition_residual=decomposition.resid
ax= decomposition_residual.plot(figsize=(14,2))
ax.set_xlabel('Date')
ax.set_ylabel('Residual of time series')
ax.set_title('Residual values of the time series')
plt.show()

decomposition_trend=decomposition.trend
ax= decomposition_trend.plot(figsize=(14,2))
ax.set_xlabel('Date')
ax.set_ylabel('Trend of time series')
ax.set_title('Trend values of the time series')
plt.show()

---

将频率和周期添加到季节分解中以实现平滑。它会起作用的

from pandas_datareader import data as pdr
from statsmodels.graphics import tsaplots
import statsmodels.api as sm

current_date=datetime.datetime.now()
start_date=datetime.datetime(current_date.year,1,1)
df = pdr.get_data_yahoo("MSFT",start_date,current_date).reset_index()

decomposition=sm.tsa.seasonal_decompose(x=df['High'],model='additive',         extrapolate_trend='freq', period=30)
decomposition.plot()
plt.show()

decomposition_trend=decomposition.trend
ax= decomposition_trend.plot(figsize=(14,2))
ax.set_xlabel('Date')
ax.set_ylabel('Trend of time series')
ax.set_title('Trend values of the time series')
plt.show()

decomposition_residual=decomposition.resid
ax= decomposition_residual.plot(figsize=(14,2))
ax.set_xlabel('Date')
ax.set_ylabel('Residual of time series')
ax.set_title('Residual values of the time series')
plt.show()

decomposition_trend=decomposition.trend
ax= decomposition_trend.plot(figsize=(14,2))
ax.set_xlabel('Date')
ax.set_ylabel('Trend of time series')
ax.set_title('Trend values of the time series')
plt.show()