June 2022
Intermediate to advanced
384 pages
6h 26m
Japanese
Content preview from 動かして学ぶAI・機械学習の基礎 ―TensorFlowによるコンピュータビジョン、自然言語処理、時系列データの予測とデプロイ
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10章シーケンスを予測するモデルの作成
「9章 シーケンスと時系列データの理解」では、シーケンスデータと時系列の属性（トレンド、季節性、自己相関、ノイズ）を紹介しました。予測に使用する時系列を作成し、基本的な統計的予測を行う方法を検討しました。ここからは、予測に機械学習を使う方法を学びます。しかし、モデルを作成する前に、ウィンドウデータセットを作成して、予測モデルを学習するための時系列データを構造化する方法を理解しなければなりません。
これが必要である理由を理解するために、「9章 シーケンスと時系列データの理解」で作成した時系列を考えてみましょう。それをプロットしたものが図10-1です。
図10-1 合成時系列
時間tの値を予測する場合、時間tより前の値の関数とする必要があります。例えば、タイムステップ1,200における時系列の値を、それ以前の30個の値の関数として予測します。この場合、図10-2に示すように、タイムステップ1,170から1,199までの値でタイムステップ1,200の値が決まります。
図10-2 予測に影響を与える過去の値
これで、おなじみの形となりました。1,170-1,199の値を特徴量、1,200の値をラベルと考えればいいのです。データセット内すべての既知の値に対して、特定の値を特徴量、次の値をラベルにすれば、モデルの学習に使用できるかなり適切な特徴量とラベルのセットができあがります。 ...
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