Time Series Anomaly Detection

Introduction

The TrustAlert Time Series Anomaly Detection project is a cutting-edge system designed to identify unusual patterns in health-related news coverage. By analyzing temporal patterns in news article frequencies from the GDELT database, we can detect potential disease outbreaks and emerging health concerns. Our pipeline processes news data into time series and applies sophisticated anomaly detection methods to identify significant deviations that may indicate public health events.

Key Features

• Time Series Processing: Transforms news article frequencies into robust time series data
• Multiple Detection Methods: Implements LSTM, ARIMA, and IQR-based anomaly detection
• Interactive Visualization: Provides dynamic Plotly-based visualizations of time series and detected anomalies
• Real-time Analysis: Processes streaming news data for immediate anomaly detection
• Customizable Parameters: Allows fine-tuning of detection sensitivity and analysis windows

Technologies Used

• Python: Core implementation using numpy, pandas, and scikit-learn
• Deep Learning: LSTM-based anomaly detection using TensorFlow/Keras
• Statistical Analysis: ARIMA modeling and IQR-based detection
• Interactive Visualization: Plotly for dynamic time series visualization
• Data Processing: Advanced ETL pipeline for news article processing

Use Cases

• Early Disease Outbreak Detection: Monitor unusual spikes in disease-related news coverage
• Public Health Trend Analysis: Track temporal patterns in health-related news
• Anomaly Investigation: Detailed analysis of detected anomalies with contextual information
• Real-time Monitoring: Continuous surveillance of news patterns for immediate detection

Live Demo

Input

Users can upload CSV files containing time series data with two columns:

• Dates: Timeline of news article publications
• Counts: Frequency of disease-related mentions

How it works

1. Time series data is processed and normalized
2. Users select their preferred anomaly detection method
3. The system applies the chosen algorithm to identify anomalies
4. Results are visualized with interactive plots highlighting detected anomalies

Output

• Interactive time series visualization
• Highlighted anomalous periods
• Confidence scores for detected anomalies
• Downloadable analysis results

Try it out

Experience the demo in action:

👉 Launch Demo

Benefits

• Early Warning System: Rapid detection of unusual health-related news patterns
• Data-Driven Insights: Statistical and machine learning-based anomaly detection
• Flexible Analysis: Multiple detection methods for different use cases
• Visual Analytics: Interactive visualization for pattern exploration
• Automated Monitoring: Continuous surveillance of news patterns

Integration

This time series analysis module is a core component of the TrustAlert ecosystem, working alongside news classification and health monitoring systems. The anomaly detection results feed into a comprehensive health surveillance platform that combines multiple data sources for enhanced public health monitoring.