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Autonomous選手権発表資料(株式会社フェイスポート)
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Autonomous選手権発表資料(株式会社フェイスポート)
1.
Autonomous Database で台風について 調べてみました
2.
ADWとDVDを接続
3.
1951~2017年の台風発生数推移 発 生 数 年度
4.
DVML Bi Python (拡張機能)を入れる ※1クリックのみでインストール完了します。
5.
予測する 例:FORECAST (XSA('weblogic'.'test')."Columns"."発生数 ", (XSA('weblogic'.'test')."Columns"."年度 ",XSA('weblogic'.'test')."Columns"."月"), 'low', numPeriods=3;modelType=ets;) FORECAST (Y,X,予測したい期間,精度,予測方法)
6.
予測 実際
7.
傾向を調べる 例) TRENDLINE(XSA('weblogic'.'縦2台風発生 数generation')."Columns"."数", (XSA('weblogic'.'縦2台風発生数 generation')."Columns"."年度") BY (XSA('weblogic'.'縦2台風発生数 generation')."Columns"."月"), 'LINEAR', 'VALUE') TRENDLINE (Y,X,形式,値の形式,)
8.
trendline 揺らぎが収まるので、分析だけでなく、 会議資料などの用途にも
9.
1971年から2017年の 台風データを入れる 1 発生数 実際に発生した数 2 接近数 中心が300KM以内に近づいた数 3
上陸数 中心が上陸した数 ※国土交通省 気象庁「台風の統計資料」より
10.
結果 発生数 接近数 上陸
11.
マイ計算追加 上陸率 少数の値が上下し、見えにくい
12.
上陸率にトレンドをかける 上陸率 トレンド 値の上下がなくなる。11%ほどと判明
13.
トレンドの周期ある程度 見えているとき 周期区切り トレンド トレンドのかけ方を試すことも楽しい
14.
稲の被害について追加 上陸数 稲の被害 ※農林水産省「作物統計」より水稲・陸稲被害 明らかな相関関係
15.
米の物価について追加 ※農林水産省「農業物価類別価格指数」 より米を抜粋 上陸数 米物価 相関関係なし、価格には直接影響しないか
16.
干害について追加 上陸数 干害 相関関係なし一気に降っても干害は解消しない ※農林水産省「作物統計」より干害
17.
コシヒカリ一等の割合について追加 上陸数 一等 の比率 相関関係多少、米の質にも影響する ※農林水産省「米の検査結果」よりコシヒカリ
18.
漁獲量について追加※農林水産省「漁獲・養殖業部門別統計」 より総生産量 上陸数 漁獲量 相関関係多少、漁獲量にも影響
19.
有効求人倍率について追加 有効 求人 倍率 発生数 発生数を有効求人倍率が2年遅れで追いかける ※総務省統計局「労働力調査」より 有効求人倍率
20.
完全失業率について追加 完全 失業率 発生数 相関関係なし、、ということは ※総務省統計局「労働力調査」より 失業率
21.
結論 ・台風の上陸数の多い年は明らかに稲の被害額 が多くなり、米等級を維持する負担も増大する が、価格には影響しない →農家の皆様、本当にありがとうございます ・台風の発生数が多い年の2年後、有効求人倍 率が上がるが、失業率には影響しない →台風が来る→仕事が増える→人が足りなくな る→人事が人を集め始める →再来年あたり人の取り合いが来る
22.
Data Visualizationの魅力 1 GUIで簡単操作できる 2
予め関数を作成しておけば、 値を変えることは容易 ※管理系の部署の人も使用できる。 もっと手軽に、普段の社内資料を作るような 用途でも。 3 接続の容易さ Autonomous DBとの接続やCSVをドロップ
23.
今までに分析に全く関わっていなかった 企業こそメリットあり 開発 (主軸) データ 分析展開 データ 分析 データ 分析 人的リソースや顧客の状況 に合わせて自由 にスケールアップダウン