Merlion vs Tensorflow Lite

Lightweight solution for mobile and embedded devices; Enables low-latency inference of on-device machine learning models with a small binary size; Fast performance

Standardized and easily extensible data loading & benchmarking for a wide range of forecasting and anomaly detection datasets; A library of diverse models for both anomaly detection and forecasting, unified under a shared interface. Models include classic statistical methods, tree ensembles, and deep learning approaches. Advanced users may fully configure each model as desired; Abstract DefaultDetector and DefaultForecaster models that are efficient, robustly achieve good performance, and provide a starting point for new users; AutoML for automated hyperaparameter tuning and model selection; Practical, industry-inspired post-processing rules for anomaly detectors that make anomaly scores more interpretable, while also reducing the number of false positives