Python 如何扩展线性回归线并预测未来？

我使用包scipy.stats生成线性回归线，如下所示：

from scipy.stats import linregress
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt 

value = np.array([51, 54, 66, 58, 25, 78, 69, 35, 95]) 
year = np.array([2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018]) 

y = pd.Series(value) 
x = pd.Series(year)

slope, intercept, r_value, p_value, std_err = linregress(x, y)

line = [slope*xi + intercept for xi in x]

plt.plot(x, line, color = 'orange', label="Fitting Line", linewidth=1)
plt.scatter(x, y, s = 5, marker = '.', label="Sample Point", color = 'dodgerblue')
plt.show()

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从目前的阶段来看，回归线只涵盖了现有的药剂，但我希望预测十年后的价值。有没有其他有效的方法可以直接在图表上显示预测线（实际上是延长线）？

您可以为预测定义一组新的x值

years_extended = np.arange(2010, 2028, 1)

y = pd.Series(value) 
x = pd.Series(year)

slope, intercept, r_value, p_value, std_err = linregress(x, y)

line = [slope*xi + intercept for xi in years_extended]

plt.plot(years_extended, line, color = 'orange', label="Fitting Line", linewidth=1)
plt.scatter(x, y, s = 5, marker = '.', label="Sample Point", color = 'dodgerblue')
plt.xticks(range(2010, 2029, 2))

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您可以为预测定义一组新的x值

years_extended = np.arange(2010, 2028, 1)

y = pd.Series(value) 
x = pd.Series(year)

slope, intercept, r_value, p_value, std_err = linregress(x, y)

line = [slope*xi + intercept for xi in years_extended]

plt.plot(years_extended, line, color = 'orange', label="Fitting Line", linewidth=1)
plt.scatter(x, y, s = 5, marker = '.', label="Sample Point", color = 'dodgerblue')
plt.xticks(range(2010, 2029, 2))

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非常感谢您的回答！高效简单的回答，谢谢你！高效简单