July 2018
Intermediate to advanced
596 pages
13h 35m
Japanese
Content preview from Pythonによるデータ分析入門 第2版 ―NumPy、pandasを使ったデータ処理
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390
11
章 時系列データ
2011-10-13 388.826429 25.961429 73.905000
2011-10-14 391.038000 26.048667 74.185333
[2292 rows x 3 columns]
11.7.1
指数加重関数
等しく重み付けされた観測値に対して静的な窓のサイズを使う方法の代替手段には、一定の減衰因
子を使ってより最近の観測値を強く重み付けする、というやり方があります。減衰因子を指定する方法
はいろいろありますが、一般的なのは、
span
を使うものです。
span
を使うと、移動する窓関数のサイ
ズを
span
と同じ値にした場合の結果と比較できます。
指数関数的に重み付けされた統計値は、より最近の観測値を重み付けするので、観測値が均等に重
み付けされている場合よりも変化に適応するのが早くなります。
pandas
は
rolling
や
expanding
と同様に、
ewm
も持っています。次の例は、
Apple
の株価で
span=60
で指数加重を取ったものと
60
日移動平均を比較したものです（図11-7）。
In [249]: aapl_px = close_px.AAPL['2006':'2007']
In [250]: ma60 = aapl_px.rolling(30, min_periods=20).mean()
In [251]: ewma60 = aapl_px.ewm(span=30).mean()
In [252]: ma60.plot(style='k--', ...
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