如何通过对数据趋势应用线性回归来找出斜率值？

Question

我有一个时间序列数据，我可以从中找出 trend。现在我需要放置一条最适合趋势数据的回归线，并且想知道斜率是否是 +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 data.Below 中的 trend 是找出 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 是每天的日期，Y 是每个日期的趋势数据 day.Now 我想应用线性回归来找到回归线并找出斜率是否是+ve 或 -ve 或 constant.I 已尝试以下代码

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

我希望结果变量会有一些关于因变量或自变量、斜率或 intercepts.But 我得到以下错误的值

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

我在某些网站上得到了上面的代码片段，但我无法在我的 case.What 中应用，我做错了吗？

Answer 1

您的问题在这里：

X = df.index.strftime('%Y-%m-%d')

X 因此是一个字符串，因此您不能使用它来拟合回归。你会想要像

这样的东西

X = (df.index.astype(np.int64) // 10**9).values 这会将您的日期时间转换为 Unix 秒。

或者，如果您更愿意为 X 使用 "days since initial value" 之类的东西，您可以

start_date = df.index[0]
X = (df.index - start_date).days.values

无论哪种情况，您都需要打印 results.summary() 而不是 results。

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