import numpy as np import matplotlib.pyplot as plt t = np.arange (1, 10, 1) # Generate a list of time series from 1 to 9 y = t * 0.9 + np.sin (t) # Generate an upward volatility y-axis Data list model = np.polyfit (t, y, deg = 2) #Fit the second-order model. If deg = 1, the first-order linear regression model t2 = np.arange (-2, 12, 0.5) # Generate a new time series list y2predict = np.polyval (model, t2) #Use the newly generated model model to generate the independent variable y2predict plt.plot (t, y, ' under the condition that t2 is the dependent variable o ', t2, y2predict,' x ') plt.show ()
