Question

我有一个时间序列数据，我可以从中找到trend。现在我需要设置一个最符合趋势数据的回归线，并想知道斜率是否为+ ve或-ve或constant.Below是我的csv文件，其中包含数据

 date,cpu
2018-02-10 11:52:59.342269+00:00,6.0
2018-02-10 11:53:04.006971+00:00,6.0
2018-02-10 22:35:33.438948+00:00,4.0
2018-02-10 22:35:37.905242+00:00,4.0
2018-02-11 12:01:00.663084+00:00,4.0
2018-02-11 12:01:05.136107+00:00,4.0
2018-02-11 12:31:00.228447+00:00,5.0
2018-02-11 12:31:04.689054+00:00,5.0
2018-02-11 13:01:00.362877+00:00,5.0
2018-02-11 13:01:04.824231+00:00,5.0
2018-02-11 23:42:40.304334+00:00,0.0
2018-02-11 23:44:27.357619+00:00,0.0
2018-02-12 01:38:25.012175+00:00,7.0
2018-02-12 01:53:39.721800+00:00,8.0
2018-02-12 01:53:53.310947+00:00,8.0
2018-02-12 01:56:37.657977+00:00,8.0
2018-02-12 01:56:45.133701+00:00,8.0
2018-02-12 04:49:36.028754+00:00,9.0
2018-02-12 04:49:40.097157+00:00,9.0
2018-02-12 07:20:52.148437+00:00,9.0
...          ...                 ...

首先，我需要找出给定数据中的trend。下面是查找trend

的代码

df = pd.read_csv("test_forecast/cpu_data.csv")
df["date"] = pd.to_datetime(df["date"], format="%Y-%m-%d")
df.set_index("date", inplace=True)
df = df.resample('D').mean().interpolate(method='linear', axis=0).fillna(0)

X = df.index.strftime('%Y-%m-%d')
Y = sm.tsa.seasonal_decompose(df["cpu"]).trend.interpolate(method='linear', axis=0).fillna(0).values

所以X是每日日期，Y是每天的趋势数据。现在我想应用线性回归来查找回归线并找出斜率是+ ve还是 - ve或constant.I尝试过下面的代码

model = sm.OLS(y,X, missing='drop')
results = model.fit()
print(results)

我希望结果变量有一些关于相关或自变量，斜率或截距的值。但是我得到以下错误

Traceback (most recent call last):
  File "/home/souvik/PycharmProjects/Pandas/test11.py", line 37, in <module>
    model = sm.OLS(y,X, missing='drop')
  File "/home/souvik/data_analysis/lib/python3.5/site-packages/statsmodels/regression/linear_model.py", line 817, in __init__
    hasconst=hasconst, **kwargs)
  File "/home/souvik/data_analysis/lib/python3.5/site-packages/statsmodels/regression/linear_model.py", line 663, in __init__
    weights=weights, hasconst=hasconst, **kwargs)
  File "/home/souvik/data_analysis/lib/python3.5/site-packages/statsmodels/regression/linear_model.py", line 179, in __init__
    super(RegressionModel, self).__init__(endog, exog, **kwargs)
  File "/home/souvik/data_analysis/lib/python3.5/site-packages/statsmodels/base/model.py", line 212, in __init__
    super(LikelihoodModel, self).__init__(endog, exog, **kwargs)
  File "/home/souvik/data_analysis/lib/python3.5/site-packages/statsmodels/base/model.py", line 64, in __init__
    **kwargs)
  File "/home/souvik/data_analysis/lib/python3.5/site-packages/statsmodels/base/model.py", line 87, in _handle_data
    data = handle_data(endog, exog, missing, hasconst, **kwargs)
  File "/home/souvik/data_analysis/lib/python3.5/site-packages/statsmodels/base/data.py", line 633, in handle_data
    **kwargs)
  File "/home/souvik/data_analysis/lib/python3.5/site-packages/statsmodels/base/data.py", line 79, in __init__
    self._handle_constant(hasconst)
  File "/home/souvik/data_analysis/lib/python3.5/site-packages/statsmodels/base/data.py", line 131, in _handle_constant
    ptp_ = self.exog.ptp(axis=0)
TypeError: cannot perform reduce with flexible type

我在某些网站上获得了上面的代码片段，但我无法申请。我的错误是什么？

Answer 1

你的问题在这里：

JAXBElement

因此X是一个字符串，因此您无法使用它来拟合回归。你会想要像

这样的东西

X = df.index.strftime('%Y-%m-%d')会将您的日期时间转换为Unix秒。

或者，如果您更喜欢使用X = (df.index.astype(np.int64) // 10**9).values的“自初始值以来的天数”，您可以

在任何一种情况下，您都需要打印start_date = df.index[0] X = (df.index - start_date).days.values而不是results.summary()。

如何通过对数据趋势进行线性回归来找出斜率值？

1 个答案: