问题
I have some data that were recoreded yearly as follows.
mydata = [0.6619346141815186, 0.7170140147209167, 0.692265510559082, 0.6394098401069641, 0.6030995845794678, 0.6500746607780457, 0.6013327240943909, 0.6273292303085327, 0.5865356922149658, 0.6477396488189697, 0.5827181339263916, 0.6496025323867798, 0.6589270234107971, 0.5498126149177551, 0.48638370633125305, 0.5367399454116821, 0.517595648765564, 0.5171639919281006, 0.47503289580345154, 0.6081966757774353, 0.5808742046356201, 0.5856912136077881, 0.5608134269714355, 0.6400936841964722, 0.6766082644462585]
corresponding_year = [1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994]]
I used statsmodels
python package to calculate lowess as follows.
import statsmodels.api as sm
lowess = sm.nonparametric.lowess
z = lowess(x, y, frac= 1./3, it=3)
The output I got was as follows.
[[1.96000000e+03, 6.95703548e-01],
[1.96100000e+03, 6.81750671e-01],
[1.96200000e+03, 6.68002318e-01],
[1.96300000e+03, 6.55138324e-01],
[1.96400000e+03, 6.38960761e-01],
[1.96500000e+03, 6.25042177e-01],
[1.96600000e+03, 6.18586936e-01],
[1.96700000e+03, 6.17026334e-01],
[1.96800000e+03, 6.14565102e-01],
[1.96900000e+03, 6.17610340e-01],
[1.97000000e+03, 6.20404414e-01],
[1.97100000e+03, 6.10193222e-01],
[1.97200000e+03, 5.90100648e-01],
[1.97300000e+03, 5.70935248e-01],
[1.97400000e+03, 5.47818726e-01],
[1.97500000e+03, 5.25788570e-01],
[1.97600000e+03, 5.18661218e-01],
[1.97700000e+03, 5.28921300e-01],
[1.97800000e+03, 5.42783400e-01],
[1.97900000e+03, 5.55425915e-01],
[1.98000000e+03, 5.71486587e-01],
[1.98100000e+03, 5.91539778e-01],
[1.98200000e+03, 6.13021691e-01],
[1.98300000e+03, 6.34508409e-01],
[1.98400000e+03, 6.57703989e-01]]
However, I am not clear what are the two values I get in statsmodel
. Is there something I make wrong. Moreover, I would also like to know what the two paramers frac
and it
do?
Moreover, I would also like to plot the smoothed timeseries using seaborn
. It seems like seaborn supports lowess
. However, it does not have the frac
and it
parameters. See the code below.
import numpy as np
import seaborn as sns
x = np.arange(0, 10, 0.01)
ytrue = np.exp(-x / 5) + 2 * np.sin(x / 3)
y = ytrue + np.random.normal(size=len(x))
sns.regplot(x, y, lowess=True)
In that case, is it possible to draw regplot
in seaborn
using statmodels
output?
I am happy to provide more details if needed.
回答1:
The lowess result can be plotted as shown in the code below. Note that lowess()
first argument is the y
-value (endog
) and the second is the x
(exog
). The default result has z[:,0]
being the sorted x
-values and z[:,1]
the corresponding estimated y
-values.
import matplotlib.pyplot as plt
import statsmodels.api as sm
import numpy as np
mydata = [0.6619346141815186, 0.7170140147209167, 0.692265510559082, 0.6394098401069641, 0.6030995845794678, 0.6500746607780457, 0.6013327240943909, 0.6273292303085327, 0.5865356922149658, 0.6477396488189697, 0.5827181339263916, 0.6496025323867798, 0.6589270234107971, 0.5498126149177551, 0.48638370633125305, 0.5367399454116821, 0.517595648765564, 0.5171639919281006, 0.47503289580345154, 0.6081966757774353, 0.5808742046356201, 0.5856912136077881, 0.5608134269714355, 0.6400936841964722, 0.6766082644462585]
corresponding_year = [1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994]
x = np.array(corresponding_year)
y = np.array(mydata)
z = sm.nonparametric.lowess(y, x, frac= 1./3, it=3)
plt.plot(x, y, color='dodgerblue')
plt.plot(z[:,0], z[:,1], 'ro-')
plt.show()
PS: To compare to the seaborn regplot
on the same plot, call it as:
sns.regplot(x, y, lowess=True, ax=plt.gca())
来源:https://stackoverflow.com/questions/60985292/how-to-smooth-timeseries-with-yearly-data-with-lowess-in-python