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aeon
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Aplicar ML de séries temporais com toolkit Aeon
Também disponível em: davila7
Dados de séries temporais requerem algoritmos especializados além do machine learning padrão. Aeon fornece APIs compatíveis com scikit-learn para classificação, regressão, clustering, previsão, detecção de anomalias, segmentação e busca de similaridade em dados temporais.
Suporta: Claude Codex Code(CC)
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A utilizar "aeon". Classify my time series data using aeon
Resultado esperado:
- RocketClassifier trained successfully
- Accuracy on test set: 92.3%
- Key parameters: n_kernels=10000
- Data shape verified: (samples, channels, timepoints)
Auditoria de Segurança
Segurov4 • 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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⚡ Contém scripts (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
🌐 Acesso à rede (6)
⚙️ Comandos externos (473)
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🔑 Variáveis de ambiente (1)
Auditado por: claude Ver Histórico de Auditoria →
Pontuação de qualidade
45
Arquitetura
100
Manutenibilidade
83
Conteúdo
20
Comunidade
100
Segurança
91
Conformidade com especificações
O Que Você Pode Construir
Construir modelos preditivos
Classificar leituras de sensores, prever falhas de equipamentos ou prever tendências de vendas com algoritmos especializados de séries temporais.
Analisar padrões temporais
Detectar anomalias em sinais fisiológicos, segmentar dados genômicos ou encontrar motivos repetidos em sequências experimentais.
Escalar pipelines de séries temporais
Integrar com pipelines sklearn, comparar resultados de benchmark e implementar modelos de séries temporais prontos para produção.
Tente Estes Prompts
Classificação básica
Use aeon to classify the time series data in X_train, y_train. Train a RocketClassifier and evaluate accuracy on X_test.
Detecção de anomalias
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.
Previsão
Train an ARIMA forecaster (order=(1,1,1)) on my training data and predict the next 5 values.
Otimização de pipeline
Create an sklearn pipeline with Normalizer, RocketTransformer, and a GradientBoostingClassifier using aeon transformers and sklearn estimators.
Melhores Práticas
- Normalizar dados de séries temporais antes de aplicar a maioria dos algoritmos usando aeon.transformations.collection.Normalizer
- Começar com MiniRocketClassifier ou RocketClassifier para prototipagem rápida antes de testar métodos de deep learning
- Validar performance do modelo usando validação cruzada e comparar com métodos baseline como 1-NN Euclidiano
Evitar
- Usar classificadores de deep learning em datasets pequenos com menos de 100 amostras
- Pular normalização de dados ao trabalhar com DTW ou outras medidas de distância elástica
- Ignorar o formato de entrada necessário (n_samples, n_channels, n_timepoints)
Perguntas Frequentes
Qual é o formato de entrada para aeon?
Aeon espera séries temporais no formato (n_samples, n_channels, n_timepoints). Dados univariados devem ter n_channels=1.
Qual classificador é mais rápido?
MiniRocketClassifier oferece a melhor relação velocidade-performance. RocketClassifier também é rápido com mais kernels.
Posso usar pipelines sklearn?
Sim, transformadores e classificadores aeon são totalmente compatíveis com sklearn Pipeline e GridSearchCV.
Como detectar anomalias?
Use STOMP ou LeftSTAMP de aeon.anomaly_detection. Fit e chame fit_predict para obter scores de anomalia.
Quais distâncias estão disponíveis?
DTW, DDTW, WDTW, Euclidiano, Manhattan, LCSS, ERP, EDR, TWE, MSM e Shape-DTW.
Como lidar com valores ausentes?
Use SimpleImputer de aeon.transformations.collection antes de aplicar classificadores ou forecasters.
Detalhes do Desenvolvedor
Estrutura de arquivos