python 中的趋势或偏移检测？

Question

Is there any library which detects trends and shift in data automatically in python, I have searched on the internet but could not find any library.

Nor I have found any working examples, I found some papers but they weren't that helpful.

So if anyone knows a library or practical implementation for pls do suggest me.

Answer 1

Have you had a look at scipy ? Self-description:

"SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering."

There is also a tutorial about detrending data with scipy.

import numpy as np
from matplotlib import pyplot as plt
from scipy import signal

n = 150
t = np.linspace(0, 10, n)
x_raw = .4 * t + np.random.normal(size=n)

x = signal.detrend(x_raw)

plt.figure(figsize=(5, 4))
plt.plot(t, x_raw, label="Raw data")
plt.plot(t, x, label="After Trend Removal")
plt.legend(loc='best')
plt.show()

Update:

Sadly there is no argument for signaltools.detrend to directly get the trend. Of course, one could simple edit the method or do a linear regression by hand.

But the quickest way to implement this seems to be this:

import numpy as np
from matplotlib import pyplot as plt
from scipy import signal

n = 150
t = np.linspace(0, 10, n)
x_raw = .4 * t + np.random.normal(size=n)

x = signal.detrend(x_raw)
d = x_raw - x

is_positive_trend = d[-1] > d[0]
m = "+" if is_positive_trend else "-"

plt.figure(figsize=(5, 4))
plt.plot(t, x_raw, label=f"Raw data ({m})")
plt.plot(t, d)
plt.legend(loc='best')
plt.show()

Calulate the delta d = x_raw - x between raw data and detrended data. The trend is positive, when the last x -value is larger than the first: is_positive_trend = d[-1] > d[0]