Question

我遇到布林带算法问题。我想将此算法应用于我的时间序列数据。

代码：

length = 1440

dataframe = pd.DataFrame(speed)

ave = pd.stats.moments.rolling_mean(speed,length)

sd = pd.stats.moments.rolling_std(speed,length=1440)

upband = ave + (sd*2)

dnband = ave - (sd*2)

print np.round(ave,3), np.round(upband,3), np.round(dnband,3)

输入：

speed=[96.5, 97.0, 93.75, 96.0, 94.5, 95.0, 94.75, 96.0, 96.5, 97.0, 94.75, 97.5, 94.5, 96.0, 92.75, 96.5, 91.5, 97.75, 93.0, 96.5, 92.25, 95.5, 92.5, 95.5, 94.0, 96.5, 94.25, 97.75, 93.0]

“ave”变量的结果：

[1440行x 1列] 0 0 NaN 1 NaN 2 NaN 3 NaN 4 NaN 5 NaN 6 NaN 7 NaN 8 NaN 9 NaN 10 NaN 11 NaN 12 NaN 13 NaN 14 NaN 15 NaN 16 NaN 17 NaN

Answer 1

第一点是，正如我在评论中已经提到的，rolling_mean需要一个DataFrame 你可以通过插入行来实现这一点

speed = pd.DataFrame(data=speed)

在ave = ...行之前。尽管如此，您还错过了在rolling_std中定义window属性（见：http://pandas.pydata.org/pandas-docs/stable/generated/pandas.rolling_std.html）

AttributeError：'list'对象没有属性'dtype'

1 个答案: