August 2015
Intermediate to advanced
272 pages
5h 46m
Japanese
Content preview from Think Stats 第2版 ―プログラマのための統計入門
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182
12 章 時系列分析
12.6
系列相関
価格は
1
日ごとに変わるので、何かパターンがあるのではないかと期待されます。
月曜に価格が高ければ、その後数日間は高いと期待できます。もし価格が低ければ、
しばらく低いだろうと期待します。このようなパターンは、値のそれぞれが系列の次
の値と相関するので、系列相関（
serial correlation
）と呼ばれます。
系列相関を計算するには、時系列をラグ（
lag
）と呼ぶ間隔だけ横にずらして、元
のものとの相関を計算します。
def SerialCorr(series, lag=1):
xs = series[lag:]
ys = series.shift(lag)[lag:]
corr = thinkstats2.Corr(xs, ys)
return cor r
ずらした後の最初の
lag
値は
nan
なので、
Corr
の計算の前にスライスしてそれを
取り除いています
†
。
lag=1
で元の価格データに対して
SerialCorr
を適用すると、高品質カテゴリでは
系列相関が
0.48
、中品質で
0.16
、低品質で
0.10
になります。時系列の長期傾向では、
系列相関が強く観察されるものと期待できます。例えば、価格が低下傾向だと、時系
列の前半は値が平均より上で、後半では平均より下だと期待できます。
傾向を引き去っても、相関が残るかどうかはさらに興味深いところです。例えば、
EWMA
の残差を計算してから、その系列相関を計算できます。
ewma = pandas.ewma(reindexed.ppg, ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.