我有一个月间隔的小时间序列。我想把它画出来,然后分解成季节性,趋势,残差。我从导入csv到pandas开始,而不是仅仅绘制出工作正常的时间序列。我遵循This教程,代码如下:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
ali3 = pd.read_csv('C:\\Users\\ALI\\Desktop\\CSV\\index\\ZIAM\\ME\\ME_DATA_7_MONTH_AVG_PROFIT\\data.csv',
names=['Date', 'Month','AverageProfit'],
index_col=['Date'],
parse_dates=True)
\* Delete month column which is a string */
del ali3['Month']
ali3
plt.plot(ali3)
在这个阶段,我试着这样做季节分解:
^{pr2}$这将导致以下错误:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-41-afeab639d13b> in <module>()
1 import statsmodels.api as sm
----> 2 res = sm.tsa.seasonal_decompose(ali3.AverageProfit)
3 fig = res.plot()
C:\Users\D063375\AppData\Local\Continuum\Anaconda2\lib\site-packages\statsmodels\tsa\seasonal.py in seasonal_decompose(x, model, filt, freq)
86 filt = np.repeat(1./freq, freq)
87
---> 88 trend = convolution_filter(x, filt)
89
90 # nan pad for conformability - convolve doesn't do it
C:\Users\D063375\AppData\Local\Continuum\Anaconda2\lib\site-packages\statsmodels\tsa\filters\filtertools.py in convolution_filter(x, filt, nsides)
287
288 if filt.ndim == 1 or min(filt.shape) == 1:
--> 289 result = signal.convolve(x, filt, mode='valid')
290 elif filt.ndim == 2:
291 nlags = filt.shape[0]
C:\Users\D063375\AppData\Local\Continuum\Anaconda2\lib\site-packages\scipy\signal\signaltools.py in convolve(in1, in2, mode)
468 return correlate(volume, kernel[slice_obj].conj(), mode)
469 else:
--> 470 return correlate(volume, kernel[slice_obj], mode)
471
472
C:\Users\D063375\AppData\Local\Continuum\Anaconda2\lib\site-packages\scipy\signal\signaltools.py in correlate(in1, in2, mode)
158
159 if mode == 'valid':
--> 160 _check_valid_mode_shapes(in1.shape, in2.shape)
161 # numpy is significantly faster for 1d
162 if in1.ndim == 1 and in2.ndim == 1:
C:\Users\D063375\AppData\Local\Continuum\Anaconda2\lib\site-packages\scipy\signal\signaltools.py in _check_valid_mode_shapes(shape1, shape2)
70 if not d1 >= d2:
71 raise ValueError(
---> 72 "in1 should have at least as many items as in2 in "
73 "every dimension for 'valid' mode.")
74
ValueError: in1 should have at least as many items as in2 in every dimension for 'valid' mode.
有谁能告诉我我做错了什么,我该怎么解决它?非常感谢。在
编辑:这就是数据帧的样子
Date AverageProfit
2015-06-01 29.990231
2015-07-01 26.080038
2015-08-01 25.640862
2015-09-01 25.346447
2015-10-01 27.386001
2015-11-01 26.357709
2015-12-01 25.260644
你有7个数据点,这通常是一个非常小的数字,用于执行平稳性分析。在
你没有足够的点数来使用季节性分解。要看到这一点,您可以将数据连接起来,以创建一个扩展的时间序列(只需重复接下来几个月的数据)。让
extendedData
成为这个扩展数据帧,data
是您的原始数据。在^{pr2}$
季节性估计的频率(
freq
)是从数据中自动估计出来的,并且可以手动指定。在您可以尝试获取第一个差异:生成一个新的时间序列,从上一个数据值中减去每个数据值。在你的情况下,它看起来像这样:
接下来可以应用平稳性测试,如here
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