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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180
12 章 時系列分析
図
12-3
1
日ごとの価格とローリング平均（左）と指数加重移動平均（右）
ewma = pandas.ewma(reindexed.ppg, span=30)
thinkplot.Plot(ewma.index, ewma)
スパン（
span
）パラメータは、大まかには、ウィンドウサイズに相当します。こ
れは重みの低下速度を制御するので、各平均について、無視できない点の個数を決定
します。
図
12-3
の右側は同じデータに対する
EWMA
を示します。両方ともに定義済みの
ところでは、ローリング平均と同じようですが、欠損値がありません。したがって、
処理しやすくなります。時系列の始まるところでは、データ点の個数がより少ないの
でノイズが多くなります。
12.5
欠損値
時系列の傾向が得られたので、次のステップは季節要因、すなわち周期的振る舞い
の検討です。人間の振る舞いに基づく時系列データは、日次、週次、月次、年次の周
期を示すことがよくあります。次節では、季節変動を検定する手法を示しますが、こ
れは、欠損値があるとうまく動かないので、欠損値問題をまず解決しなければいけま
せん。
単純でよく使うのは、移動平均を使って欠損値を埋めるものです。
オブジェクト
Series
のメソッド
fillna
がまさに望んでいることをやってくれます。
2010/11
2011/5
2011/11
2012/5
2012/11
2013/5
2013/11
2014/5
2010/11
2011/5
2011/11
2012/5
2012/11 ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.