Research Paper

Core Innovation

This paper introduces an unsupervised anomaly detection algorithm combining Haar discrete wavelet transforms with a specially designed t-test. It addresses challenges of class imbalance and label scarcity, providing a theoretically grounded method that outperforms existing unsupervised and self-supervised benchmarks on extensive datasets.

Why It Matters

Detecting anomalies quickly and accurately in time series data is essential for preventing failures and security breaches in critical industries. This solution reduces false positives and dependence on costly labeled data, improving operational efficiency and safety. It scales across diverse sectors with complex, imbalanced datasets.

Potential Customers & Pain Points

• Cybersecurity firms – Need reliable threat detection with low false alarms
• Financial institutions – Require early fraud detection despite scarce labeled anomalies
• Healthcare providers – Demand accurate monitoring of patient data without extensive labeling
• Manufacturing plants – Seek fast fault detection to minimize downtime
• IoT system operators – Need scalable anomaly detection for vast sensor data.

Business Model

SaaS subscription model offering API access and platform integration with tiered pricing based on data volume and feature set.

Competitive Landscape

• Anodot
• SAS Visual Investigator
• Splunk
• DataRobot
• Amazon Lookout for Metrics

Implementation Challenges

• Integration with existing enterprise data pipelines
• Convincing customers to adopt new unsupervised methods over established supervised tools
• Handling domain-specific anomaly definitions and thresholds

Validation Strategy

• Pilot deployments with cybersecurity and manufacturing clients to benchmark detection accuracy and false positive rates
• Comparative studies against leading anomaly detection platforms on real-world datasets
• Scalability testing on large IoT sensor networks