Pattern Analysis

Pattern analysis is the process of identifying recurring trends, structures, or behaviors within a set of data. It involves examining data points to uncover consistent patterns or anomalies that may not be immediately obvious. This technique is widely used in fields like machine learning, data mining, and predictive analytics to make sense of large datasets. By recognizing these patterns, organizations can forecast future events, detect unusual activity, or improve decision-making processes. For example, pattern analysis is used in detecting fraud, predicting market trends, and optimizing operational processes. The ability to identify and understand patterns is critical for gaining insights, improving efficiency, and enabling data-driven strategies across various industries. The goal of pattern analysis is to transform raw data into actionable knowledge by revealing relationships and behaviors that can influence outcomes.

Example Code

const zendarAI = require('zendarai')

// Function to analyze metrics using ZendarAI
async function analyzeMetrics(metrics) {
  try {
    // Perform the analysis on the provided metrics data
    const insights = await zendarAI.analyze('metrics', {
      data: metrics.raw,               // Raw data for analysis
      detect: ['anomalies', 'trends'],  // What to detect (anomalies and trends)
      threshold: 0.95,                  // Sensitivity threshold
      timeFrame: 'lastMonth',           // Time frame for the analysis
      include: ['summary', 'detailedReport'], // What to include in the results
      outputFormat: 'json',             // Output format as JSON
      generateLogs: true,               // Generate logs for the analysis
      logErrors: true,                  // Log any errors encountered
      notifyOnCompletion: true         // Notify when the analysis is complete
    });

    // Check if the analysis was successful
    if (insights) {
      console.log('Analysis complete. Insights generated:');
      console.log('Summary:', insights.summary);
      console.log('Detailed Report:', insights.detailedReport);

      // If anomalies or trends are detected, display them
      if (insights.anomalies && insights.anomalies.length > 0) {
        console.log('Anomalies detected:', insights.anomalies);
      }
      if (insights.trends && insights.trends.length > 0) {
        console.log('Trends detected:', insights.trends);
      }
    } else {
      console.log('No insights generated. Please check the input data.');
    }
  } catch (error) {
    console.error('Error analyzing metrics:', error);
  }
}

// Example metrics data
const metrics = {
  raw: [
    { date: '2025-01-01', value: 100 },
    { date: '2025-01-02', value: 120 },
    { date: '2025-01-03', value: 130 },
    // More data points...
  ]
};

// Call the analyzeMetrics function with the example data
analyzeMetrics(metrics);