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Zeitreihen-ML mit dem Aeon-Toolkit anwenden
Auch verfügbar von: davila7
Zeitreihendaten erfordern spezialisierte Algorithmen jenseits des Standard-Machine-Learning. Aeon bietet scikit-learn-kompatible APIs für Klassifikation, Regression, Clustering, Vorhersage, Anomalieerkennung, Segmentierung und Similaritätssuche auf zeitlichen Daten.
Unterstützt: Claude Codex Code(CC)
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Teste es
Verwendung von "aeon". Classify my time series data using aeon
Erwartetes Ergebnis:
- RocketClassifier trained successfully
- Accuracy on test set: 92.3%
- Key parameters: n_kernels=10000
- Data shape verified: (samples, channels, timepoints)
Sicherheitsaudit
Sicherv4 • 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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⚡ Enthält Skripte (8)
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🌐 Netzwerkzugriff (6)
⚙️ Externe Befehle (473)
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🔑 Umgebungsvariablen (1)
Auditiert von: claude Audit-Verlauf anzeigen →
Qualitätsbewertung
45
Architektur
100
Wartbarkeit
83
Inhalt
20
Community
100
Sicherheit
91
Spezifikationskonformität
Was du bauen kannst
Prädiktive Modelle erstellen
Klassifiziere Sensordaten, sage Ausfälle von Geräten voraus oder prognostiziere Verkaufstrends mit spezialisierten Zeitreihenalgorithmen.
Temporale Muster analysieren
Erkenne Anomalien in physiologischen Signalen, segmentiere genomische Daten oder finde wiederholte Motive in experimentellen Sequenzen.
Zeitreihen-Pipelines skalieren
Integriere mit sklearn-Pipelines, vergleiche Benchmark-Ergebnisse und setze produktionsreife Zeitreihenmodelle ein.
Probiere diese Prompts
Grundlegende Klassifikation
Use aeon to classify the time series data in X_train, y_train. Train a RocketClassifier and evaluate accuracy on X_test.
Anomalieerkennung
Detect anomalies in my time series y using aeon. Use the STOMP detector with window_size=50 and return indices where anomaly_scores exceed the 95th percentile.
Prognose
Train an ARIMA forecaster (order=(1,1,1)) on my training data and predict the next 5 values.
Pipeline-Optimierung
Create an sklearn pipeline with Normalizer, RocketTransformer, and a GradientBoostingClassifier using aeon transformers and sklearn estimators.
Bewährte Verfahren
- Normalisiere Zeitreihendaten vor der Anwendung der meisten Algorithmen mit aeon.transformations.collection.Normalizer
- Starte mit MiniRocketClassifier oder RocketClassifier für schnelles Prototyping, bevor du Deep-Learning-Methoden ausprobierst
- Validiere die Modellperformance mit Kreuzvalidierung und vergleiche mit Baseline-Methoden wie 1-NN Euclidean
Vermeiden
- Deep-Learning-Klassifikatoren auf kleinen Datensätzen mit weniger als 100 Samples verwenden
- Datennormalisierung überspringen, wenn mit DTW oder anderen elastischen Distanzmaßen gearbeitet wird
- Die erforderliche Eingabeform (n_samples, n_channels, n_timepoints) ignorieren
Häufig gestellte Fragen
Welches Eingabeformat erwartet aeon?
Aeon erwartet Zeitreihen im Format (n_samples, n_channels, n_timepoints). univariate Daten sollten n_channels=1 haben.
Welcher Klassifikator ist am schnellsten?
MiniRocketClassifier bietet das beste Geschwindigkeits-Performance-Verhältnis. RocketClassifier ist auch schnell mit mehr Kernels.
Kann ich sklearn-Pipelines verwenden?
Ja, aeon-Transformatoren und -Klassifikatoren sind vollständig kompatibel mit sklearn Pipeline und GridSearchCV.
Wie erkennt man Anomalien?
Verwende STOMP oder LeftSTAMP aus aeon.anomaly_detection. Führe fit aus und rufe fit_predict auf, um Anomalie-Scores zu erhalten.
Welche Distanzen sind verfügbar?
DTW, DDTW, WDTW, Euclidean, Manhattan, LCSS, ERP, EDR, TWE, MSM und Shape-DTW.
Wie gehe ich mit fehlenden Werten um?
Verwende SimpleImputer aus aeon.transformations.collection vor der Anwendung von Klassifikatoren oder Prognosemodellen.