将不规则时间戳的测量值转换为等间距的时间加权平均值

问题描述

我有一系列带有时间戳且间隔不规则的测量值.这些系列中的值始终代表测量值的变化——即没有变化就没有新值.此类系列的一个简单示例是:

I have series of measurements which are time stamped and irregularly spaced. Values in these series always represent changes of the measurement -- i.e. without a change no new value. A simple example of such a series would be:

23:00:00.100     10
23:00:01.200      8
23:00:01.600      0
23:00:06.300      4

我想要达到的是一系列等距的时间加权平均值.对于给定的示例，我可能会针对基于秒的频率，因此结果如下:

What I want to reach is an equally spaced series of time-weighted averages. For the given example I might aim at a frequency based on seconds and hence a result like the following:

23:00:01     NaN ( the first 100ms are missing )
23:00:02     5.2 ( 10*0.2 + 8*0.4 + 0*0.4 )
23:00:03       0
23:00:04       0
23:00:05       0
23:00:06     2.8 ( 0*0.3 + 4*0.7 )

我正在寻找解决该问题的 Python 库.对我来说，这似乎是一个标准问题，但目前我在 pandas 等标准库中找不到这样的功能.

I am searching for a Python library solving that problem. For me, this seems to be a standard problem, but I couldn't find such a functionality so far in standard libraries like pandas.

算法需要考虑两件事:

• 时间加权平均
• 在形成平均值时考虑当前间隔之前的值(甚至可能在领先者之前)

data.resample('S', fill_method='pad')          # forming a series of seconds

做部分工作.为聚合提供用户定义的函数将允许 形成时间加权平均值，但是因为忽略了区间的开始，所以这个平均值也是不正确的.更糟糕的是:系列中的孔被平均值填充，在上面的示例中导致第 3、4 和 5 秒的值不为零.

does parts of the work. Providing a user-defined function for aggregation will allow to form time-weighted averages, but because the beginning of the interval is ignored, this average will be incorrect too. Even worse: the holes in the series are filled with the average values, leading in the example from above to the values of seconds 3, 4 and 5 to be non zero.

data = data.resample('L', fill_method='pad')   # forming a series of milliseconds
data.resample('S')

以一定的准确性完成这个技巧，但是 - 取决于准确性 - 非常昂贵.就我而言，太贵了.

does the trick with a certain accurateness, but is -- depending on the accurateness -- very expensive. In my case, too expensive.

import pandas as pa
import numpy as np
from datetime import datetime
from datetime import timedelta

time_stamps=[datetime(2013,04,11,23,00,00,100000), 
             datetime(2013,04,11,23,00,1,200000),
             datetime(2013,04,11,23,00,1,600000),
             datetime(2013,04,11,23,00,6,300000)]
values = [10, 8, 0, 4]
raw = pa.TimeSeries(index=time_stamps, data=values)

def round_down_to_second(dt):
    return datetime(year=dt.year, month=dt.month, day=dt.day, 
                    hour=dt.hour, minute=dt.minute, second=dt.second)

def round_up_to_second(dt):
    return round_down_to_second(dt) + timedelta(seconds=1)

def time_weighted_average(data):
    end = pa.DatetimeIndex([round_up_to_second(data.index[-1])])
    return np.average(data, weights=np.diff(data.index.append(end).asi8))

start = round_down_to_second(time_stamps[0])
end = round_down_to_second(time_stamps[-1])
range = pa.date_range(start, end, freq='S')
data = raw.reindex(raw.index + range)
data = data.ffill()

data = data.resample('S', how=time_weighted_average)

推荐答案

您可以使用 traces 来做到这一点.

You can do this with traces.

from datetime import datetime
import traces

ts = traces.TimeSeries(data=[
    (datetime(2016, 9, 27, 23, 0, 0, 100000), 10),
    (datetime(2016, 9, 27, 23, 0, 1, 200000), 8),
    (datetime(2016, 9, 27, 23, 0, 1, 600000), 0),
    (datetime(2016, 9, 27, 23, 0, 6, 300000), 4),
])

regularized = ts.moving_average(
    start=datetime(2016, 9, 27, 23, 0, 1),
    sampling_period=1,
    placement='left',
)

这会导致:

[(datetime(2016, 9, 27, 23, 0, 1), 5.2),
 (datetime(2016, 9, 27, 23, 0, 2), 0.0),
 (datetime(2016, 9, 27, 23, 0, 3), 0.0),
 (datetime(2016, 9, 27, 23, 0, 4), 0.0),
 (datetime(2016, 9, 27, 23, 0, 5), 0.0),
 (datetime(2016, 9, 27, 23, 0, 6), 2.8)]

这篇关于将不规则时间戳的测量值转换为等间距的时间加权平均值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持跟版网！