المهارات aeon
T
aeon
آمن ⚡
يحتوي على سكربتات🌐
الوصول إلى الشبكة⚙️
الأوامر الخارجية🔑
متغيرات البيئة
تطبيق التعلم الآلي السلاسل الزمنية باستخدام مجموعة أدوات Aeon
متاح أيضًا من: davila7
تتطلب البيانات السلاسل الزمنية خوارزميات متخصصة تتجاوز التعلم الآلي القياسي. توفر Aeon واجهات برمجة تطبيقات متوافقة مع scikit-learn للتصنيف والانحدار والتنبؤ والكشف عن الشذوذ والتجزئة والبحث عن التشابه على البيانات الزمنية.
يدعم: Claude Codex Code(CC)
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تنزيل ZIP المهارة
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رفع في Claude
اذهب إلى Settings → Capabilities → Skills → Upload skill
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فعّل وابدأ الاستخدام
اختبرها
استخدام "aeon". Classify my time series data using aeon
النتيجة المتوقعة:
- RocketClassifier trained successfully
- Accuracy on test set: 92.3%
- Key parameters: n_kernels=10000
- Data shape verified: (samples, channels, timepoints)
التدقيق الأمني
آمنv4 • 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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الملفات التي تم فحصها
5,466
الأسطر التي تم تحليلها
4
النتائج
4
إجمالي عمليات التدقيق
عوامل الخطر
⚡ يحتوي على سكربتات (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
🌐 الوصول إلى الشبكة (6)
⚙️ الأوامر الخارجية (473)
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🔑 متغيرات البيئة (1)
تم تدقيقه بواسطة: claude عرض سجل التدقيق →
درجة الجودة
45
الهندسة المعمارية
100
قابلية الصيانة
83
المحتوى
20
المجتمع
100
الأمان
91
الامتثال للمواصفات
ماذا يمكنك بناءه
بناء نماذج تنبؤية
تصنيف قراءات المستشعرات، أو التنبؤ بأعطال المعدات، أو توقع اتجاهات المبيعات باستخدام خوارزميات السلاسل الزمنية المتخصصة.
تحليل الأنماط الزمنية
كشف الشذوذ في الإشارات الفسيولوجية، أو تجزئة البيانات الجينومية، أو العثور على النماذج المتكررة في التسلسلات التجريبية.
توسيع خطوط أنابيب السلاسل الزمنية
التكامل مع خطوط أنابيب sklearn، ومقارنة نتائج المعايير، ونشر نماذج السلاسل الزمنية الجاهزة للإنتاج.
جرّب هذه الموجهات
التصنيف الأساسي
Use aeon to classify the time series data in X_train, y_train. Train a RocketClassifier and evaluate accuracy on X_test.
كشف الشذوذ
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.
التنبؤ
Train an ARIMA forecaster (order=(1,1,1)) على بيانات التدريب الخاصة بي وتوقع القيم الـ 5 التالية.
تحسين خط الأنابيب
Create an sklearn pipeline with Normalizer, RocketTransformer, and a GradientBoostingClassifier using aeon transformers and sklearn estimators.
أفضل الممارسات
- قم بتطبيع السلاسل الزمنية قبل تطبيق معظم الخوارزميات باستخدام aeon.transformations.collection.Normalizer
- ابدأ بـ MiniRocketClassifier أو RocketClassifier للتطوير السريع قبل تجربة طرق التعلم العميق
- تحقق من أداء النموذج باستخدام التحقق المتقاطع وقارنه بالأساليب الأساسية مثل 1-NN Euclidean
تجنب
- استخدام مصنفات التعلم العميق على مجموعات بيانات صغيرة تقل عن 100 عينة
- تخطي تطبيع البيانات عند العمل مع DTW أو的其他 مقاييس المسافة المرنة
- تجاهل شكل الإدخال المطلوب (n_samples, n_channels, n_timepoints)
الأسئلة المتكررة
ما هو تنسيق الإدخال لـ aeon؟
تتوقع Aeon السلاسل الزمنية بتنسيق (n_samples, n_channels, n_timepoints). يجب أن تكون البيانات أحادية المتغير n_channels=1.
أي مصنف هو الأسرع؟
يوفر MiniRocketClassifier أفضل توازن بين السرعة والأداء. RocketClassifier سريع أيضاً مع نواة أكثر.
هل يمكنني استخدام خطوط أنابيب sklearn؟
نعم، محولات ومصنفات Aeon متوافقة بالكامل مع sklearn Pipeline و GridSearchCV.
كيفية كشف الشذوذ؟
استخدم STOMP أو LeftSTOMP من aeon.anomaly_detection. قم بالتدريب واستدعاء fit_predict للحصول على درجات الشذوذ.
ما هي المسافات المتاحة؟
DTW, DDTW, WDTW, Euclidean, Manhattan, LCSS, ERP, EDR, TWE, MSM, و Shape-DTW.
كيفية التعامل مع القيم المفقودة؟
استخدم SimpleImputer من aeon.transformations.collection قبل تطبيق المصنفات أو منشئي التنبؤ.