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Valtronics Team edited this page May 4, 2026 · 1 revision

Analytics API Documentation

Complete API reference for analytics and reporting system

Overview

The Analytics API provides comprehensive functionality for data analysis, reporting, and insights generation in the Valtronics system. This API supports real-time analytics, historical data analysis, and AI-powered insights.

Base Endpoint

/api/v1/analytics/

Authentication

All Analytics API endpoints require JWT authentication:

Authorization: Bearer <jwt-token>

Endpoints

1. System Analytics

Get comprehensive system-wide analytics and statistics.

Endpoint: GET /api/v1/analytics/system

Query Parameters:

Parameter Type Default Description
time_range string 24h Time range (1h/6h/24h/7d/30d)
include_device_details boolean false Include detailed device information
include_telemetry boolean true Include telemetry statistics

Example Request:

curl -X GET "http://localhost:8000/api/v1/analytics/system?time_range=24h&include_device_details=true" \
  -H "Authorization: Bearer <token>"

Example Response:

{
  "success": true,
  "data": {
    "system_stats": {
      "total_devices": 5,
      "online_devices": 4,
      "offline_devices": 1,
      "error_devices": 0,
      "warning_devices": 1,
      "devices_by_type": {
        "sensor": 4,
        "actuator": 1
      },
      "devices_by_status": {
        "online": 4,
        "offline": 1
      }
    },
    "device_performance": [
      {
        "device_id": 1,
        "device_name": "Temperature Sensor Alpha",
        "device_type": "sensor",
        "status": "online",
        "data_points_24h": 1440,
        "last_seen": "2024-01-01T12:00:00Z",
        "uptime_percentage": 99.5,
        "avg_response_time": 0.05,
        "error_rate": 0.01
      }
    ],
    "telemetry_overview": {
      "total_points": 7200,
      "active_devices": 5,
      "device_types": {
        "sensor": 5760,
        "actuator": 1440
      },
      "metrics": {
        "temperature": {
          "count": 2880,
          "min": 18.0,
          "max": 28.9,
          "avg": 23.4,
          "stddev": 2.1,
          "trend": "stable"
        },
        "humidity": {
          "count": 2880,
          "min": 35.2,
          "max": 65.8,
          "avg": 48.7,
          "stddev": 8.3,
          "trend": "increasing"
        }
      }
    },
    "system_health": {
      "overall_score": 0.85,
      "device_health": 0.80,
      "data_health": 0.90,
      "status": "healthy"
    },
    "utilization_trends": {
      "trend": "stable",
      "current_utilization": 20.0,
      "hourly_distribution": {
        "0": 20,
        "1": 18,
        "2": 22,
        "3": 19,
        "4": 21,
        "5": 20,
        "6": 23,
        "7": 20,
        "8": 19,
        "9": 21,
        "10": 20,
        "11": 22,
        "12": 20,
        "13": 18,
        "14": 21,
        "15": 20,
        "16": 19,
        "17": 22,
        "18": 20,
        "19": 21,
        "20": 20,
        "21": 23,
        "22": 19,
        "23": 21
      }
    }
  }
}

2. Device Analytics

Get detailed analytics for specific devices.

Endpoint: GET /api/v1/analytics/devices

Query Parameters:

Parameter Type Default Description
device_ids string null Comma-separated device IDs
device_type string null Filter by device type
status string null Filter by status
time_range string 24h Time range (1h/6h/24h/7d/30d)
metrics string null Comma-separated metrics to include
include_telemetry boolean true Include telemetry data
include_alerts boolean true Include alert statistics

Example Request:

curl -X GET "http://localhost:8000/api/v1/analytics/devices?device_ids=1,2,3&time_range=24h&metrics=temperature,humidity" \
  -H "Authorization: Bearer <token>"

Example Response:

{
  "success": true,
  "data": {
    "devices": [
      {
        "device_id": 1,
        "device_name": "Temperature Sensor Alpha",
        "device_type": "sensor",
        "status": "online",
        "location": "Zone A - Server Room",
        "performance": {
          "uptime_percentage": 99.5,
          "data_points_24h": 1440,
          "avg_response_time": 0.05,
          "error_rate": 0.01,
          "last_seen": "2024-01-01T12:00:00Z"
        },
        "telemetry": {
          "temperature": {
            "count": 1440,
            "min": 18.0,
            "max": 28.9,
            "avg": 23.4,
            "stddev": 2.1,
            "latest_value": 23.5,
            "trend": "stable"
          },
          "humidity": {
            "count": 1440,
            "min": 35.2,
            "max": 65.8,
            "avg": 48.7,
            "stddev": 8.3,
            "latest_value": 45.2,
            "trend": "decreasing"
          }
        },
        "alerts": {
          "total_alerts": 3,
          "active_alerts": 1,
          "critical_alerts": 0,
          "warning_alerts": 1,
          "info_alerts": 1,
          "resolved_alerts": 2
        },
        "health_score": 0.85
      }
    ],
    "summary": {
      "total_devices": 3,
      "avg_uptime": 98.7,
      "total_data_points": 4320,
      "total_alerts": 7
    }
  }
}

3. Performance Metrics

Get detailed performance metrics for the system.

Endpoint: GET /api/v1/analytics/performance

Query Parameters:

Parameter Type Default Description
time_range string 24h Time range (1h/6h/24h/7d/30d)
granularity string 1h Data granularity (1m/5m/15m/1h/1d)
metrics string all Comma-separated metrics
include_system boolean true Include system metrics

Example Request:

curl -X GET "http://localhost:8000/api/v1/analytics/performance?time_range=24h&granularity=1h&metrics=cpu,memory,network" \
  -H "Authorization: Bearer <token>"

Example Response:

{
  "success": true,
  "data": {
    "time_range": "24h",
    "granularity": "1h",
    "system_metrics": {
      "cpu": {
        "data_points": 24,
        "min": 15.2,
        "max": 45.8,
        "avg": 28.7,
        "stddev": 8.3,
        "trend": "stable",
        "unit": "%"
      },
      "memory": {
        "data_points": 24,
        "min": 30.5,
        "max": 65.2,
        "avg": 45.8,
        "stddev": 10.2,
        "trend": "increasing",
        "unit": "%"
      },
      "network": {
        "data_points": 24,
        "min": 1024,
        "max": 5120,
        "avg": 2048,
        "stddev": 1024,
        "trend": "stable",
        "unit": "KB/s"
      },
      "disk": {
        "data_points": 24,
        "min": 15.2,
        "max": 85.6,
        "avg": 45.3,
        "stddev": 18.7,
        "trend": "increasing",
        "unit": "%"
      }
    },
    "application_metrics": {
      "api_response_time": {
        "data_points": 24,
        "min": 45,
        "max": 234,
        "avg": 89,
        "stddev": 35,
        "trend": "stable",
        "unit": "ms"
      },
      "database_query_time": {
        "data_points": 24,
        "min": 12,
        "max": 156,
        "avg": 45,
        "stddev": 28,
        "trend": "stable",
        "unit": "ms"
      },
      "websocket_connections": {
        "data_points": 24,
        "min": 0,
        "max": 15,
        "avg": 8,
        "stddev": 4,
        "trend": "stable",
        "unit": "connections"
      }
    },
    "performance_score": 0.82
  }
}

4. Trend Analysis

Get trend analysis for various metrics over time.

Endpoint: GET /api/v1/analytics/trends

Query Parameters:

Parameter Type Default Description
metric string required Metric to analyze
device_id integer null Filter by device ID
time_range string 7d Time range (1h/6h/24h/7d/30d)
granularity string 1h Data granularity
trend_type string linear Trend type (linear/exponential/seasonal)
forecast_points integer 0 Number of forecast points

Example Request:

curl -X GET "http://localhost:8000/api/v1/analytics/trends?metric=temperature&device_id=1&time_range=7d&granularity=1h&trend_type=linear&forecast_points=24" \
  -H "Authorization: Bearer <token>"

Example Response:

{
  "success": true,
  "data": {
    "metric": "temperature",
    "device_id": 1,
    "time_range": "7d",
    "granularity": "1h",
    "trend_analysis": {
      "trend_type": "linear",
      "slope": 0.02,
      "intercept": 23.1,
      "r_squared": 0.87,
      "trend_direction": "increasing",
      "confidence": 0.85
    },
    "historical_data": [
      {
        "timestamp": "2024-01-01T00:00:00Z",
        "value": 23.1,
        "predicted": 23.1
      },
      {
        "timestamp": "2024-01-01T01:00:00Z",
        "value": 23.3,
        "predicted": 23.12
      }
    ],
    "forecast": [
      {
        "timestamp": "2024-01-08T00:00:00Z",
        "value": 24.5,
        "confidence_interval": {
          "lower": 23.8,
          "upper": 25.2
        }
      }
    ],
    "statistics": {
      "data_points": 168,
      "min": 18.5,
      "max": 28.9,
      "avg": 23.4,
      "stddev": 2.1,
      "seasonality": {
        "detected": false,
        "seasonal_period": null
      }
    },
    "anomalies": [
      {
        "timestamp": "2024-01-03T14:00:00Z",
        "value": 28.9,
        "anomaly_score": 0.92,
        "type": "spike"
      }
    ]
  }
}

5. Generate Reports

Generate various types of reports.

Endpoint: POST /api/v1/analytics/reports

Request Body:

{
  "report_type": "string (required)",
  "time_range": "string (required)",
  "format": "string (required)",
  "device_ids": "array (optional)",
  "metrics": "array (optional)",
  "filters": "object (optional)",
  "options": "object (optional)"
}

Example Request:

curl -X POST http://localhost:8000/api/v1/analytics/reports \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{
    "report_type": "device_performance",
    "time_range": "7d",
    "format": "json",
    "device_ids": [1, 2, 3],
    "metrics": ["temperature", "humidity", "pressure"],
    "filters": {
      "status": "online",
      "device_type": "sensor"
    },
    "options": {
      "include_charts": true,
      "include_summary": true,
      "include_recommendations": true
    }
  }'

Example Response:

{
  "success": true,
  "data": {
    "report_id": "report_12345",
    "report_type": "device_performance",
    "time_range": "7d",
    "generated_at": "2024-01-01T12:00:00Z",
    "format": "json",
    "summary": {
      "total_devices": 3,
      "avg_uptime": 98.7,
      "total_data_points": 3024,
      "total_alerts": 12,
      "performance_score": 0.85
    },
    "device_details": [
      {
        "device_id": 1,
        "device_name": "Temperature Sensor Alpha",
        "performance": {
          "uptime_percentage": 99.5,
          "data_points": 1008,
          "avg_response_time": 0.05,
          "health_score": 0.92
        },
        "metrics": {
          "temperature": {
            "avg": 23.4,
            "min": 18.5,
            "max": 28.9,
            "trend": "stable"
          }
        },
        "alerts": {
          "total": 4,
          "critical": 0,
          "warning": 2,
          "info": 2
        }
      }
    ],
    "charts": [
      {
        "chart_type": "line",
        "title": "Temperature Trend",
        "data": [
          {"timestamp": "2024-01-01T00:00:00Z", "value": 23.1},
          {"timestamp": "2024-01-01T01:00:00Z", "value": 23.3}
        ]
      }
    ],
    "recommendations": [
      {
        "type": "maintenance",
        "priority": "medium",
        "description": "Consider calibrating temperature sensor Alpha",
        "device_id": 1
      }
    ]
  },
  "message": "Report generated successfully"
}

6. Data Quality Analysis

Analyze data quality metrics.

Endpoint: GET /api/v1/analytics/data-quality

Query Parameters:

Parameter Type Default Description
device_id integer null Filter by device ID
time_range string 24h Time range (1h/6h/24h/7d/30d)
metric string null Filter by metric name

Example Request:

curl -X GET "http://localhost:8000/api/v1/analytics/data-quality?device_id=1&time_range=24h" \
  -H "Authorization: Bearer <token>"

Example Response:

{
  "success": true,
  "data": {
    "device_id": 1,
    "time_range": "24h",
    "overall_quality_score": 0.92,
    "metrics": {
      "completeness": {
        "expected_points": 1440,
        "actual_points": 1435,
        "completeness_rate": 0.997,
        "missing_points": 5,
        "gaps": [
          {
            "start": "2024-01-01T03:00:00Z",
            "end": "2024-01-01T03:05:00Z",
            "duration": 300
          }
        ]
      },
      "accuracy": {
        "validation_errors": 2,
        "outlier_count": 3,
        "accuracy_score": 0.98,
        "outliers": [
          {
            "timestamp": "2024-01-01T14:00:00Z",
            "value": 28.9,
            "expected_range": [18.0, 30.0],
            "z_score": 2.6
          }
        ]
      },
      "timeliness": {
        "avg_delay": 30,
        "max_delay": 120,
        "timeliness_score": 0.95,
        "delay_distribution": {
          "p50": 25,
          "p95": 45,
          "p99": 85
        }
      },
      "consistency": {
        "variance": 2.1,
        "consistency_score": 0.89,
        "pattern_analysis": {
          "regular_pattern": true,
          "seasonal_pattern": false,
          "anomaly_count": 2
        }
      }
    },
    "recommendations": [
      {
        "type": "data_collection",
        "priority": "low",
        "description": "Investigate missing data points at 3:00 AM"
      },
      {
        "type": "sensor_calibration",
        "priority": "medium",
        "description": "Check sensor calibration for outlier values"
      }
    ]
  }
}

7. Usage Analytics

Get system usage analytics.

Endpoint: GET /api/v1/analytics/usage

Query Parameters:

Parameter Type Default Description
time_range string 24h Time range (1h/6h/24h/7d/30d)
granularity string 1h Data granularity
include_api boolean true Include API usage
include_ui boolean true Include UI usage
include_devices boolean true Include device usage

Example Request:

curl -X GET "http://localhost:8000/api/v1/analytics/usage?time_range=24h&granularity=1h" \
  -H "Authorization: Bearer <token>"

Example Response:

{
  "success": true,
  "data": {
    "time_range": "24h",
    "granularity": "1h",
    "api_usage": {
      "total_requests": 15420,
      "unique_users": 25,
      "avg_requests_per_hour": 642,
      "peak_hour": "14:00",
      "peak_requests": 892,
      "endpoints": {
        "/api/v1/devices/": 5120,
        "/api/v1/telemetry/": 3840,
        "/api/v1/alerts/": 2560,
        "/api/v1/analytics/": 1280,
        "/api/v1/health/": 2560
      },
      "response_times": {
        "p50": 45,
        "p95": 89,
        "p99": 156,
        "avg": 67
      }
    },
    "ui_usage": {
      "total_sessions": 180,
      "unique_users": 15,
      "avg_session_duration": 1800,
      "pages_visited": {
        "/dashboard": 890,
        "/devices": 450,
        "/analytics": 320,
        "/alerts": 280
      },
      "user_engagement": {
        "avg_page_views": 8.5,
        "bounce_rate": 0.15,
        "return_visits": 0.65
      }
    },
    "device_usage": {
      "total_devices": 5,
      "active_devices": 4,
      "data_points_submitted": 7200,
      "avg_data_points_per_hour": 300,
      "most_active_device": {
        "device_id": 1,
        "data_points": 2880,
        "percentage": 40.0
      }
    },
    "storage_usage": {
      "database_size": "2.5 GB",
      "telemetry_data_size": "1.8 GB",
      "growth_rate": "0.5 GB/day",
      "retention_days": 30
    }
  }
}

8. Comparative Analytics

Compare performance between devices or time periods.

Endpoint: POST /api/v1/analytics/comparison

Request Body:

{
  "comparison_type": "string (required)",
  "entities": "array (required)",
  "time_range": "string (required)",
  "metrics": "array (required)",
  "options": "object (optional)"
}

Example Request:

curl -X POST http://localhost:8000/api/v1/analytics/comparison \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{
    "comparison_type": "device",
    "entities": [1, 2, 3],
    "time_range": "7d",
    "metrics": ["temperature", "humidity", "uptime"],
    "options": {
      "include_charts": true,
      "include_statistics": true
    }
  }'

Example Response:

{
  "success": true,
  "data": {
    "comparison_type": "device",
    "entities": [
      {
        "id": 1,
        "name": "Temperature Sensor Alpha",
        "metrics": {
          "temperature": {
            "avg": 23.4,
            "min": 18.5,
            "max": 28.9,
            "stddev": 2.1
          },
          "humidity": {
            "avg": 48.7,
            "min": 35.2,
            "max": 65.8,
            "stddev": 8.3
          },
          "uptime": {
            "percentage": 99.5
          }
        }
      },
      {
        "id": 2,
        "name": "Pressure Monitor Beta",
        "metrics": {
          "temperature": {
            "avg": 22.8,
            "min": 17.2,
            "max": 27.5,
            "stddev": 2.8
          },
          "humidity": {
            "avg": 46.2,
            "min": 32.1,
            "max": 62.3,
            "stddev": 7.9
          },
          "uptime": {
            "percentage": 98.2
          }
        }
      }
    ],
    "comparisons": {
      "temperature": {
        "best_device": 1,
        "worst_device": 2,
        "difference": 0.6,
        "percentage_difference": 2.6
      },
      "humidity": {
        "best_device": 2,
        "worst_device": 1,
        "difference": 2.5,
        "percentage_difference": 5.1
      },
      "uptime": {
        "best_device": 1,
        "worst_device": 2,
        "difference": 1.3,
        "percentage_difference": 1.3
      }
    },
    "ranking": {
      "overall": [
        {"device_id": 1, "score": 0.92},
        {"device_id": 2, "score": 0.85},
        {"device_id": 3, "score": 0.78}
      ]
    }
  },
  "message": "Comparison completed successfully"
}

9. Anomaly Detection

Detect anomalies in telemetry data.

Endpoint: POST /api/v1/analytics/anomaly-detection

Request Body:

{
  "device_id": "integer (required)",
  "metric": "string (required)",
  "time_range": "string (required)",
  "sensitivity": "string (required)",
  "algorithm": "string (optional)",
  "options": "object (optional)"
}

Example Request:

curl -X POST http://localhost:8000/api/v1/analytics/anomaly-detection \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{
    "device_id": 1,
    "metric": "temperature",
    "time_range": "24h",
    "sensitivity": "medium",
    "algorithm": "statistical",
    "options": {
      "window_size": 24,
      "threshold": 2.0
    }
  }'

Example Response:

{
  "success": true,
  "data": {
    "device_id": 1,
    "metric": "temperature",
    "time_range": "24h",
    "algorithm": "statistical",
    "sensitivity": "medium",
    "anomalies": [
      {
        "timestamp": "2024-01-01T14:00:00Z",
        "value": 28.9,
        "expected_range": [18.0, 30.0],
        "z_score": 2.6,
        "anomaly_score": 0.92,
        "type": "spike",
        "severity": "medium",
        "description": "Temperature spike detected"
      },
      {
        "timestamp": "2024-01-01T22:00:00Z",
        "value": 17.8,
        "expected_range": [18.0, 30.0],
        "z_score": -2.1,
        "anomaly_score": 0.85,
        "type": "dip",
        "severity": "low",
        "description": "Temperature dip detected"
      }
    ],
    "statistics": {
      "total_points": 1440,
      "anomaly_count": 2,
      "anomaly_rate": 0.0014,
      "avg_z_score": 0.35,
      "max_z_score": 2.6
    },
    "recommendations": [
      {
        "type": "investigate",
        "priority": "medium",
        "description": "Investigate temperature spike at 14:00"
      }
    ]
  },
  "message": "Anomaly detection completed"
}

10. Export Analytics Data

Export analytics data in various formats.

Endpoint: GET /api/v1/analytics/export

Query Parameters:

Parameter Type Default Description
report_type string system Report type (system/devices/performance/usage)
format string json Export format (json/csv/xlsx/pdf)
time_range string 24h Time range (1h/6h/24h/7d/30d)
device_ids string null Comma-separated device IDs
metrics string null Comma-separated metrics
limit integer 10000 Maximum records to export

Example Request:

curl -X GET "http://localhost:8000/api/v1/analytics/export?report_type=system&format=csv&time_range=24h" \
  -H "Authorization: Bearer <token>" \
  -o analytics_report.csv

Example Response (CSV):

timestamp,device_id,device_name,metric_name,metric_value,unit,status
2024-01-01T12:00:00Z,1,Temperature Sensor Alpha,temperature,23.5,°C,online
2024-01-01T12:00:00Z,1,Temperature Sensor Alpha,humidity,45.2,%,online
2024-01-01T12:00:00Z,2,Pressure Monitor Beta,pressure,1013.25,hPa,online

Data Models

System Analytics Object

{
  "system_stats": {
    "total_devices": "integer",
    "online_devices": "integer",
    "offline_devices": "integer",
    "error_devices": "integer",
    "devices_by_type": "object",
    "devices_by_status": "object"
  },
  "device_performance": "array",
  "telemetry_overview": "object",
  "system_health": "object",
  "utilization_trends": "object"
}

Trend Analysis Object

{
  "trend_analysis": {
    "trend_type": "string",
    "slope": "number",
    "intercept": "number",
    "r_squared": "number",
    "trend_direction": "string",
    "confidence": "number"
  },
  "historical_data": "array",
  "forecast": "array",
  "statistics": "object",
  "anomalies": "array"
}

Report Object

{
  "report_id": "string",
  "report_type": "string",
  "time_range": "string",
  "generated_at": "datetime (ISO format)",
  "format": "string",
  "summary": "object",
  "device_details": "array",
  "charts": "array",
  "recommendations": "array"
}

Report Types

Available Report Types

  • system: System-wide performance report
  • device_performance: Individual device performance report
  • usage: System usage statistics report
  • data_quality: Data quality analysis report
  • comparative: Comparative analysis report
  • trend: Trend analysis report
  • anomaly: Anomaly detection report

Export Formats

  • json: Structured JSON format
  • csv: Comma-separated values
  • xlsx: Excel spreadsheet
  • pdf: PDF document

Rate Limiting

Analytics API endpoints have specific rate limits:

  • System Analytics: 50 requests per minute
  • Device Analytics: 100 requests per minute
  • Generate Reports: 20 requests per minute
  • Trend Analysis: 30 requests per minute
  • Export Data: 10 requests per hour

Performance Considerations

Query Optimization

  • Use appropriate time ranges to limit data volume
  • Implement caching for frequently accessed analytics
  • Use pagination for large datasets
  • Optimize database queries with proper indexes

Data Processing

  • Process large datasets in batches
  • Use streaming for real-time analytics
  • Implement background processing for reports
  • Monitor memory usage during analysis

Best Practices

Analytics Usage

  • Use appropriate time ranges for analysis
  • Cache frequently accessed analytics data
  • Implement proper error handling for analytics requests
  • Monitor analytics performance and optimize as needed

Report Generation

  • Use appropriate report types for specific needs
  • Include relevant metrics and visualizations
  • Provide actionable insights and recommendations
  • Format reports appropriately for the audience

Data Quality

  • Monitor data quality metrics regularly
  • Implement data validation and cleansing
  • Track data completeness and accuracy
  • Address data quality issues promptly

Examples and Use Cases

System Health Monitoring

import requests

def get_system_health_analytics():
    """Get system health analytics"""
    response = requests.get(
        "http://localhost:8000/api/v1/analytics/system",
        params={
            "time_range": "24h",
            "include_device_details": True,
            "include_telemetry": True
        },
        headers={"Authorization": f"Bearer {token}"}
    )
    
    analytics = response.json()["data"]
    
    # Check system health
    health_score = analytics["system_health"]["overall_score"]
    if health_score < 0.8:
        print(f"System health score: {health_score} - requires attention")
    
    # Check device status
    total_devices = analytics["system_stats"]["total_devices"]
    offline_devices = analytics["system_stats"]["offline_devices"]
    
    if offline_devices > 0:
        print(f"{offline_devices} of {total_devices} devices are offline")
    
    return analytics

# Usage example
health_analytics = get_system_health_analytics()

Performance Trend Analysis

def analyze_performance_trends(metric, days=7):
    """Analyze performance trends for a metric"""
    response = requests.get(
        "http://localhost:8000/api/v1/analytics/trends",
        params={
            "metric": metric,
            "time_range": f"{days}d",
            "granularity": "1h",
            "trend_type": "linear",
            "forecast_points": 24
        },
        headers={"Authorization": f"Bearer {token}"}
    )
    
    trend_data = response.json()["data"]
    
    # Analyze trend
    trend_direction = trend_data["trend_analysis"]["trend_direction"]
    confidence = trend_data["trend_analysis"]["confidence"]
    
    print(f"Metric: {metric}")
    print(f"Trend: {trend_direction}")
    print(f"Confidence: {confidence:.2f}")
    
    # Check for anomalies
    anomalies = trend_data.get("anomalies", [])
    if anomalies:
        print(f"Found {len(anomalies)} anomalies")
        for anomaly in anomalies:
            print(f"  - {anomaly['timestamp']}: {anomaly['description']}")
    
    return trend_data

# Usage example
temp_trend = analyze_performance_trends("temperature", 7)

Support

For Analytics API support:

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Analytics API Documentation v1.0

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