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ai_anomaly_detection [2025/05/23 16:47] – [3. Combining with Visualization] eagleeyenebulaai_anomaly_detection [2025/06/26 18:20] (current) – [AI Anomaly Detection] eagleeyenebula
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 ====== AI Anomaly Detection ====== ====== AI Anomaly Detection ======
-* **[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**:+[[https://autobotsolutions.com/aurora/wiki/doku.php?id=ai_anomaly_detection|Wiki]]: [[https://autobotsolutions.com/god/templates/ai_anomaly_detection.html|Framework]]: [[https://github.com/AutoBotSolutions/Aurora/blob/Aurora/ai_anomaly_detection.py|GitHub]]: [[https://autobotsolutions.com/artificial-intelligence/anomaly-detection-module-proactive-insights-for-data-consistency/|Article]]: 
 The **AI Anomaly Detection** system is a Python-based utility that identifies outliers in datasets using statistical principles like standard deviation. This function is essential for finding anomalous data points that deviate significantly from the dataset's normal range. The **AI Anomaly Detection** system is a Python-based utility that identifies outliers in datasets using statistical principles like standard deviation. This function is essential for finding anomalous data points that deviate significantly from the dataset's normal range.
 +
 +
 +{{youtube>0m1Ivadc2PM?large}}
 +
  
 ===== Overview ===== ===== Overview =====
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 **Threshold for Anomalies**: **Threshold for Anomalies**:
 Data points are considered anomalies if they fall outside the range: Data points are considered anomalies if they fall outside the range:
 +<code>
 [mean - (3 * standard deviation), mean + (3 * standard deviation)] [mean - (3 * standard deviation), mean + (3 * standard deviation)]
 +</code>
 ==== 2. Logging Information ==== ==== 2. Logging Information ====
  
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 **Example Log Messages**: **Example Log Messages**:
 +<code>
 INFO: Detecting anomalies in the data... INFO: Anomalies detected: [120, -45] INFO: Detecting anomalies in the data... INFO: Anomalies detected: [120, -45]
 +</code>
 ====== Function Details ====== ====== Function Details ======
  
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 **Signature**: **Signature**:
-   python+<code> 
 +python
 def detect_anomalies(data: List[float]) -> List[float]: def detect_anomalies(data: List[float]) -> List[float]:
     """     """
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     :return: List of anomalies detected     :return: List of anomalies detected
     """     """
 +</code>
 ===== Examples ===== ===== Examples =====
  
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 **Input Example**: **Input Example**:
-   python+<code> 
 +python
 data = [10, 12, 15, 10, 11, 14, 120, 12, 9, -45] data = [10, 12, 15, 10, 11, 14, 120, 12, 9, -45]
 anomalies = detect_anomalies(data) anomalies = detect_anomalies(data)
 print(f"Anomalies: {anomalies}") print(f"Anomalies: {anomalies}")
 +</code>
 **Output**: **Output**:
- +<code>
 Anomalies: [120, -45] Anomalies: [120, -45]
-  +</code>
  
 **Explanation**: **Explanation**:
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 **Example: Empty Dataset**: **Example: Empty Dataset**:
-   python+<code> 
 +python
 data = [] data = []
 anomalies = detect_anomalies(data) anomalies = detect_anomalies(data)
 print(f"Anomalies: {anomalies}") print(f"Anomalies: {anomalies}")
-  +</code>
 **Output**: **Output**:
- +<code>
 Anomalies: [] Anomalies: []
-  +</code>
 **Explanation**: **Explanation**:
 The function immediately returns an empty list if the dataset is empty. The function immediately returns an empty list if the dataset is empty.
  
 **Example: All Data Within Range**: **Example: All Data Within Range**:
-   python+<code> 
 +python
 data = [100, 102, 98, 101, 99] data = [100, 102, 98, 101, 99]
 anomalies = detect_anomalies(data) anomalies = detect_anomalies(data)
 print(f"Anomalies: {anomalies}") print(f"Anomalies: {anomalies}")
-  +</code>
 **Output**: **Output**:
- +<code>
 Anomalies: [] Anomalies: []
-  +</code> 
  
 **Explanation**: **Explanation**:
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 **Input Data**: **Input Data**:
-   python+<code> 
 +python
 data = [100, 150, 200, 1000, 105, 210, 980, 115, 195] data = [100, 150, 200, 1000, 105, 210, 980, 115, 195]
 anomalies = detect_anomalies(data) anomalies = detect_anomalies(data)
-  +</code>
 **Output**: **Output**:
- +<code>
 Anomalies: [1000, 980] Anomalies: [1000, 980]
-  +</code>
 **Explanation**: **Explanation**:
 Outliers 1000 and 980 are classified as anomalies due to their significant deviation from the mean of the dataset. Outliers 1000 and 980 are classified as anomalies due to their significant deviation from the mean of the dataset.
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 **Framework for Live Data Streams**: **Framework for Live Data Streams**:
-   python+<code> 
 +python
 import random import random
 import time import time
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 stream_anomaly_detection() stream_anomaly_detection()
 +</code>
 ===== Advanced Usage ===== ===== Advanced Usage =====
  
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 By default, the function uses **3 standard deviations** as the threshold for anomaly detection. To customize this, modify the following part of the function: By default, the function uses **3 standard deviations** as the threshold for anomaly detection. To customize this, modify the following part of the function:
-   python+<code> 
 +python
 anomalies = [x for x in data if abs(x - mean) > THRESHOLD * std_dev] anomalies = [x for x in data if abs(x - mean) > THRESHOLD * std_dev]
-  +</code>
 **Example Custom Threshold**: **Example Custom Threshold**:
-   python+<code> 
 +python
 THRESHOLD = 2  # Using 2 standard deviations instead of 3 THRESHOLD = 2  # Using 2 standard deviations instead of 3
 data = [12, 15, 18, 10, 140] data = [12, 15, 18, 10, 140]
 anomalies = detect_anomalies(data) anomalies = detect_anomalies(data)
 print(f"Anomalies with Threshold={THRESHOLD}: {anomalies}") print(f"Anomalies with Threshold={THRESHOLD}: {anomalies}")
- +</code>
 ==== 2. Batch Detection for Multiple Data Sets ==== ==== 2. Batch Detection for Multiple Data Sets ====
  
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 **Example**: **Example**:
-   python+<code> 
 +python
 datasets = [ datasets = [
     [10, 12, 14, 18, 200],     [10, 12, 14, 18, 200],
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     anomalies = detect_anomalies(data)     anomalies = detect_anomalies(data)
     print(f"Dataset {idx + 1}: {anomalies}")     print(f"Dataset {idx + 1}: {anomalies}")
-  +</code>
 **Output**: **Output**:
- +<code>
 Dataset 1: [200] Dataset 2: [700] Dataset 3: [500] Dataset 1: [200] Dataset 2: [700] Dataset 3: [500]
 +</code>
 ==== 3. Combining with Visualization ==== ==== 3. Combining with Visualization ====
  
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 **Example with Matplotlib**: **Example with Matplotlib**:
-   python+<code> 
 +python
 import matplotlib.pyplot as plt import matplotlib.pyplot as plt
  
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 plt.legend() plt.legend()
 plt.show() plt.show()
 +</code>
 ===== Applications ===== ===== Applications =====
  
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 **3. Preprocessing for AI Pipelines**: **3. Preprocessing for AI Pipelines**:
 Flag and handle anomalous data points before model training to improve model robustness and accuracy. Flag and handle anomalous data points before model training to improve model robustness and accuracy.
- 
---- 
  
 ===== Best Practices ===== ===== Best Practices =====
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 3. **Visualization**: 3. **Visualization**:
 Combine detection results with visualizations for better interpretability. Combine detection results with visualizations for better interpretability.
- 
---- 
- 
 ===== Conclusion ===== ===== Conclusion =====
  
 The **AI Anomaly Detection** framework provides a robust, flexible, and extensible mechanism for outlier detection in numerical datasets. With applications ranging from real-time monitoring to preprocessing for AI pipelines, the system is a valuable tool for automated anomaly analysis. By leveraging advanced usage patterns like visualization and threshold adjustments, the functionality can be tailored to a wide range of industry applications. The **AI Anomaly Detection** framework provides a robust, flexible, and extensible mechanism for outlier detection in numerical datasets. With applications ranging from real-time monitoring to preprocessing for AI pipelines, the system is a valuable tool for automated anomaly analysis. By leveraging advanced usage patterns like visualization and threshold adjustments, the functionality can be tailored to a wide range of industry applications.
ai_anomaly_detection.1748018827.txt.gz · Last modified: 2025/05/23 16:47 by eagleeyenebula