Real-time anomaly detection helps you respond immediately to unusual patterns as soon as they arise. This guide provides essential information to set up, configure, and optimize your online anomaly detection process using TimeGPT, ensuring timely responses in critical scenarios.

Illustration of an online anomaly detection pipeline

Online anomaly detection with TimeGPT helps maintain system reliability by highlighting data points that may signal emerging issues or opportunities.

Code Example

import timegpt

# Load your anomaly detection model
model = timegpt.load_model("anomaly-detection")

# Sample data stream
data_stream = [100, 102, 99, 105, 250, 107, 108]  # Example data points

# Perform online anomaly detection
response = model.detect_anomalies(
data=data_stream,
window_size=3  # Number of timestamps to monitor
)

print(response)

Always validate flagged anomalies against known baseline patterns. False positives can occur, so track and refine parameters to improve accuracy.

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Additional Resources

Below are direct links to deeper dives and examples:

Maintain continuous monitoring and regularly re-evaluate your detection thresholds to ensure optimal performance in changing environments.