Python pandas 时间序列插值和正则化

2022-01-11 00:00:00 python pandas time-series interpolation regularized

问题描述

我是第一次使用 Python Pandas.我有 csv 格式的 5 分钟滞后流量数据:

I am using Python Pandas for the first time. I have 5-min lag traffic data in csv format:

...
2015-01-04 08:29:05,271238
2015-01-04 08:34:05,329285
2015-01-04 08:39:05,-1
2015-01-04 08:44:05,260260
2015-01-04 08:49:05,263711
...

有几个问题:

  • 对于某些时间戳，缺少数据 (-1)
  • 缺少条目(也是连续 2/3 小时)
  • 观察的频率并不完全是 5 分钟，但实际上偶尔会损失几秒钟

我想获得一个定期的时间序列，因此每(正好)5 分钟输入一次(并且没有缺失值).我已经成功地使用以下代码对时间序列进行了插值，以使用此代码逼近 -1 值:

I would like to obtain a regular time series, so with entries every (exactly) 5 minutes (and no missing valus). I have successfully interpolated the time series with the following code to approximate the -1 values with this code:

ts = pd.TimeSeries(values, index=timestamps)
ts.interpolate(method='cubic', downcast='infer')

如何对观察的频率进行插值和正则化?谢谢大家的帮助.

How can I both interpolate and regularize the frequency of the observations? Thank you all for the help.

解决方案

把 -1s 改成 NaNs:

Change the -1s to NaNs:

ts[ts==-1] = np.nan

然后对数据进行重新采样，使其具有 5 分钟的频率.

Then resample the data to have a 5 minute frequency. 

ts = ts.resample('5T')

请注意，默认情况下，如果两个测量值在同一个 5 分钟内，resample 会将这些值一起平均.

Note that, by default, if two measurements fall within the same 5 minute period, resample averages the values together.

最后，您可以根据时间对时间序列进行线性插值:

Finally, you could linearly interpolate the time series according to the time:

ts = ts.interpolate(method='time')

<小时>

由于您的数据看起来已经具有大约 5 分钟的频率，因此您可能需要以较短的频率重新采样，因此三次或样条插值可以平滑曲线:

Since it looks like your data already has roughly a 5-minute frequency, you might need to resample at a shorter frequency so cubic or spline interpolation can smooth out the curve:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

values = [271238, 329285, -1, 260260, 263711]
timestamps = pd.to_datetime(['2015-01-04 08:29:05',
                             '2015-01-04 08:34:05',
                             '2015-01-04 08:39:05',
                             '2015-01-04 08:44:05',
                             '2015-01-04 08:49:05'])

ts = pd.Series(values, index=timestamps)
ts[ts==-1] = np.nan
ts = ts.resample('T').mean()

ts.interpolate(method='spline', order=3).plot()
ts.interpolate(method='time').plot()
lines, labels = plt.gca().get_legend_handles_labels()
labels = ['spline', 'time']
plt.legend(lines, labels, loc='best')
plt.show()

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