A journey through research, engineering, and innovation in time series anomaly detection
Investigated time series anomaly detection challenges across e-commerce, IoT, and security domains. Reviewed 20+ papers on autoencoders, attention mechanisms, and threshold learning.
Designed hybrid LSTM-Transformer autoencoder with adaptive threshold network. Generated 100K-timestep synthetic dataset with 3 realistic anomaly types and comprehensive baselines.
Trained models on GPU, ran comprehensive benchmarks (Precision/Recall/F1/AUC-ROC), proved 5.6% improvement from adaptive thresholds via ablation study.
Built professional web app with interactive demo, real-time inference, and benchmarks dashboard. Wrote arXiv-ready research paper with methodology and results.
Combining LSTM (local patterns) with Transformers (global context) outperforms either alone. This teaches the importance of architectural diversity—no single model dominates all problem dimensions.
Learning per-timestep thresholds instead of using fixed values improved F1 by 5.6%. This demonstrates that domain-specific learning (even simple neural networks) can outperform hand-crafted rules in production systems.
Rigorously removing each component (LSTM, Transformer, adaptive thresholds) proved their individual contributions. This scientific rigor is essential for publication and demonstrates true understanding.
Fixed random seeds, documented hyperparameters, and multiple runs with error bars ensure results are trustworthy. This is why production systems demand careful engineering, not just model architecture.
All code is open source and fully reproducible. Train the model yourself in Google Colab—no GPU required (though recommended for speed).