在python中计算指数移动平均值 [英] calculate exponential moving average in python

问题描述

我有一个日期范围，并且每个日期都有一个度量值.我想计算每个日期的指数移动平均值.有人知道怎么做吗?

I have a range of dates and a measurement on each of those dates. I'd like to calculate an exponential moving average for each of the dates. Does anybody know how to do this?

我是python的新手.似乎没有将平均值内置到标准python库中，这让我感到有些奇怪.也许我找的地方不对.

I'm new to python. It doesn't appear that averages are built into the standard python library, which strikes me as a little odd. Maybe I'm not looking in the right place.

因此，给定以下代码，我该如何计算日历日期的IQ点的移动加权平均值?

So, given the following code, how could I calculate the moving weighted average of IQ points for calendar dates?

from datetime import date
days = [date(2008,1,1), date(2008,1,2), date(2008,1,7)]
IQ = [110, 105, 90]

(可能是一种更好的数据结构方式，请多多指教)

(there's probably a better way to structure the data, any advice would be appreciated)

推荐答案

似乎 mov_average_expw() scikits.timeseries.lib.movi​​ng_funcs 子模块中的 SciPy )更适合您问题的措辞.

It seems that mov_average_expw() function from scikits.timeseries.lib.moving_funcs submodule from SciKits (add-on toolkits that complement SciPy) better suits the wording of your question.

要使用平滑因子alpha计算数据的指数平滑( (1 - alpha)用维基百科的术语):

To calculate an exponential smoothing of your data with a smoothing factor alpha (it is (1 - alpha) in Wikipedia's terms):

>>> alpha = 0.5
>>> assert 0 < alpha <= 1.0
>>> av = sum(alpha**n.days * iq 
...     for n, iq in map(lambda (day, iq), today=max(days): (today-day, iq), 
...         sorted(zip(days, IQ), key=lambda p: p[0], reverse=True)))
95.0

上面的内容并不漂亮，所以让我们对其进行重构:

The above is not pretty, so let's refactor it a bit:

from collections import namedtuple
from operator    import itemgetter

def smooth(iq_data, alpha=1, today=None):
    """Perform exponential smoothing with factor `alpha`.

    Time period is a day.
    Each time period the value of `iq` drops `alpha` times.
    The most recent data is the most valuable one.
    """
    assert 0 < alpha <= 1

    if alpha == 1: # no smoothing
        return sum(map(itemgetter(1), iq_data))

    if today is None:
        today = max(map(itemgetter(0), iq_data))

    return sum(alpha**((today - date).days) * iq for date, iq in iq_data)

IQData = namedtuple("IQData", "date iq")

if __name__ == "__main__":
    from datetime import date

    days = [date(2008,1,1), date(2008,1,2), date(2008,1,7)]
    IQ = [110, 105, 90]
    iqdata = list(map(IQData, days, IQ))
    print("\n".join(map(str, iqdata)))

    print(smooth(iqdata, alpha=0.5))

示例:

$ python26 smooth.py
IQData(date=datetime.date(2008, 1, 1), iq=110)
IQData(date=datetime.date(2008, 1, 2), iq=105)
IQData(date=datetime.date(2008, 1, 7), iq=90)
95.0

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