スキル aeon
T
時系列データには標準的な機械学習を超えた特殊なアルゴリズムが必要です。Aeonは、時系列データに対する分類、回帰、クラスタリング、予測、異常検知、セグメンテーション、類似性検索向けに、scikit-learn互換のAPIを提供します。
対応: Claude Codex Code(CC)
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スキルZIPをダウンロード
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Claudeでアップロード
設定 → 機能 → スキル → スキルをアップロードへ移動
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オンにして利用開始
テストする
「aeon」を使用しています。 aeonを使用して時系列データを分類する
期待される結果:
- RocketClassifierの学習に成功
- テストセットでの精度: 92.3%
- 主要パラメータ: n_kernels=10000
- データ形状確認済み: (samples, channels, timepoints)
セキュリティ監査
安全v4 • 1/17/2026
All 531 static findings are false positives. This is a documentation-only skill containing SKILL.md and references/*.md files with no executable code. The scanner incorrectly flagged markdown syntax (backticks for inline code), Python import examples in documentation, ML algorithm names (DTW, LCSS, ERP) misinterpreted as cryptographic references, and legitimate documentation URLs. No actual code execution, network calls, or credential access exists.
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検出結果
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総監査数
リスク要因
⚡ スクリプトを含む (8)
evaluation_output.json:33 references/anomaly_detection.md:125-130 references/datasets_benchmarking.md:176-181 references/datasets_benchmarking.md:200-203 references/datasets_benchmarking.md:217-220 references/forecasting.md:111-115 references/segmentation.md:131-134 references/transformations.md:168-172
🌐 ネットワークアクセス (6)
⚙️ 外部コマンド (473)
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🔑 環境変数 (1)
監査者: claude 監査履歴を表示 →
品質スコア
45
アーキテクチャ
100
保守性
83
コンテンツ
20
コミュニティ
100
セキュリティ
91
仕様準拠
作れるもの
予測モデルの構築
センサー読み値の分類、機器の故障予測、または特殊な時系列アルゴリズムを使用した販売トレンドの予測。
時間パターンの分析
生理学的信号の異常検知、ゲノムデータのセグメンテーション、または実験シーケンスにおける繰り返しモチーフの発見。
時系列パイプラインのスケーリング
sklearnパイプラインとの統合、ベンチマーク結果の比較、本番環境向け時系列モデルのデプロイ。
これらのプロンプトを試す
基本的な分類
aeonを使用してX_train、y_trainの時系列データを分類してください。RocketClassifierを学習させ、X_testで精度を評価してください。
異常検知
aeonを使用して時系列yの異常を検知してください。window_size=50のSTOMP検出器を使用し、anomaly_scoresが95パーセンタイルを超えるインデックスを返してください。
予測
学習データでARIMA予測器（order=(1,1,1)）を学習させ、次の5つの値を予測してください。
パイプラインの最適化
Normalizer、RocketTransformer、GradientBoostingClassifierを使用してsklearnパイプラインを作成してください。aeonトランスフォーマーとsklearn推定器を使用してください。
ベストプラクティス
- aeon.transformations.collection.Normalizerを使用して、ほとんどのアルゴリズムを適用する前に時系列データを正規化する
- 深学習手法を試す前に、-fastプロトタイピングにはMiniRocketClassifierまたはRocketClassifierから始める
- 交差検証を使用してモデル性能を検証し、1-NNユークリッドなどのベースライン手法と比較する
回避
- 100サンプル未満の小さなデータセットで深学習分類器を使用する
- DTWやその他の弾性距離を使用する際にデータの正規化をスキップする
- 必要な入力形状(n_samples, n_channels, n_timepoints)を無視する
よくある質問
aeonの入力形式は何ですか？
Aeonは形状(n_samples, n_channels, n_timepoints)の時系列を想定しています。単変量データはn_channels=1にしてください。
最も高速な分類器はどれですか？
MiniRocketClassifierが最も優れた速度-性能トレードオフを提供します。RocketClassifierもより多くのカーネルで高速です。
sklearnパイプラインを使用できますか？
はい、aeonのトランスフォーマーと分類器は完全にsklearn PipelineおよびGridSearchCVと互換性があります。
異常を検知するにはどうすればよいですか？
aeon.anomaly_detectionのSTOMPまたはLeftSTAMPを使用してください。fit_predictを呼び出して異常スコアを取得します。
利用可能な距離は何ですか？
DTW、DDTW、WDTW、ユークリッド、マンハッタン、LCSS、ERP、EDR、TWE、MSM、およびShape-DTW。
欠損値を処理するにはどうすればよいですか？
分類器または予測子を適用する前に、aeon.transformations.collectionのSimpleImputerを使用してください。