如何在Python3.7中运行函数时进行时间序列分析？

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# 创建一个示例时间序列数据
dates = pd.date_range(start='2022-01-01', end='2022-12-31', freq='D')
values = np.random.randn(len(dates))

# 创建一个DataFrame对象
df = pd.DataFrame({'date': dates, 'value': values})

df.set_index('date', inplace=True)

# 绘制时间序列图
df['value'].plot()
plt.show()

# 计算移动平均值
df['moving_average'] = df['value'].rolling(window=7).mean()

# 计算指数加权移动平均值
df['ewma'] = df['value'].ewm(span=7).mean()

# 进行季节性分解
from statsmodels.tsa.seasonal import seasonal_decompose
result = seasonal_decompose(df['value'], model='additive')

# 进行时间序列预测
from statsmodels.tsa.arima.model import ARIMA
model = ARIMA(df['value'], order=(1, 1, 1))
model_fit = model.fit()
forecast = model_fit.predict(start=len(df), end=len(df)+10)