Question

考虑以下Python程序：

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

data = [["2017-05-25 22:00:00", 5],
["2017-05-25 22:05:00", 7],
["2017-05-25 22:10:00", 9],
["2017-05-25 22:15:00", 10],
["2017-05-25 22:20:00", 15],
["2017-05-25 22:25:00", 20],
["2017-05-25 22:30:00", 25],
["2017-05-25 22:35:00", 32]]

df = pd.DataFrame(data)
df.columns = ["date", "value"]
df["date2"] = pd.to_datetime(df["date"],format="%Y-%m-%d %H:%M:%S")

ts = pd.Series(df["value"].values, index=df["date2"])
mean_smoothed = ts.rolling(window=5).mean()
exp_smoothed = ts.ewm(alpha=0.5).mean()

h1 = ts.head(8)
h2 = mean_smoothed.head(8)
h3 = exp_smoothed.head(8)
k = pd.concat([h1, h2, h3], join='outer', axis=1)
k.columns = ["Actual", "Moving Average", "Exp Smoothing"]
print(k)

打印

                     Actual  Moving Average  Exp Smoothing
date2                                                     
2017-05-25 22:00:00       5             NaN       5.000000
2017-05-25 22:05:00       7             NaN       6.333333
2017-05-25 22:10:00       9             NaN       7.857143
2017-05-25 22:15:00      10             NaN       9.000000
2017-05-25 22:20:00      15             9.2      12.096774
2017-05-25 22:25:00      20            12.2      16.111111
2017-05-25 22:30:00      25            15.8      20.590551
2017-05-25 22:35:00      32            20.4      26.317647

绘制图表

plt.figure(figsize=(16,5))
plt.plot(ts, label="Original")
plt.plot(mean_smoothed, label="Moving Average")
plt.plot(exp_smoothed, label="Exponentially Weighted Average")
plt.legend()
plt.show()

移动平均值（MA）和指数平滑（ES）都会引入滞后：在上面的示例MA中，需要5个值来预测第6个值是什么。但是，如果查看表格，MA列中只有4个NaN值，第5个值已经是非NaN值（=第一个预测值）。

问题：如何在图表中绘制这些值，以便正确保留滞后？看看ES，它实际上更明显一点：ES应该从t = 2开始但是开始但是立即开始。

Answer 1

你似乎误解了移动平均线。对于MA（5），需要5个数据点来计算。一旦你收到第5点，就可以使用第1-5点计算第5点的平均值。因此，您应该只有4个NaN。

如果您想转移数据，可以尝试：

df.shift(n) # n is an integer

将“实际”移至-1，或将所有内容移至1.

Here是它的文档。

Answer 2

插值应解决问题。

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

data = [["2017-05-25 22:00:00", 5],
["2017-05-25 22:05:00", 7],
["2017-05-25 22:10:00", 9],
["2017-05-25 22:15:00", 10],
["2017-05-25 22:20:00", 15],
["2017-05-25 22:25:00", 20],
["2017-05-25 22:30:00", 25],
["2017-05-25 22:35:00", 32]]

df = pd.DataFrame(data)
df.columns = ["date", "value"]
df["date2"] = pd.to_datetime(df["date"],format="%Y-%m-%d %H:%M:%S")

ts = pd.Series(df["value"].values, index=df["date2"])
mean_smoothed = ts.rolling(window=5).mean()
###### NEW #########
mean_smoothed[0]=ts[0]
mean_smoothed.interpolate(inplace=True)
####################
exp_smoothed = ts.ewm(alpha=0.5).mean()

h1 = ts.head(8)
h2 = mean_smoothed.head(8)
h3 = exp_smoothed.head(8)
k = pd.concat([h1, h2, h3], join='outer', axis=1)
k.columns = ["Actual", "Moving Average", "Exp Smoothing"]
print(k)


plt.figure(figsize=(16,5))
plt.plot(ts, label="Original")
plt.plot(mean_smoothed, label="Moving Average")
plt.plot(exp_smoothed, label="Exponentially Weighted Average")
plt.legend()
plt.show()

Python Pandas移动平均滞后

2 个答案: