Orion is a machine-learning library built for unsupervised time series anomaly detection. Such signals are generated by a wide variety of systems, few examples include telemetry data generated by satellites, signals from wind turbines, and even stock market price tickers. We built this to provide one place where users can find the latest and greatest in machine learning and deep learning world including our own innovations. Abstract away from the users the nitty-gritty about preprocessing, finding the best pipeline, and postprocessing. We want to provide a systematic way to evaluate the latest and greatest machine learning methods via our benchmarking effort. Build time series anomaly detection platforms custom to their workflows through our backend database and rest API. A way for machine learning researchers to contribute in a scaffolded way so their innovations are immediately available to the end users.
Features
- Unsupervised time series anomaly detection (UTSAD)
- Machine learning for UTSAD
- Evaluate the latest and greatest machine learning methods
- The easiest and recommended way to install Orion is using pip
- The library makes use of a number of automated machine learning tools developed under Data to AI Lab at MIT
- Machine learning library built for unsupervised time series anomaly detection
Project Samples
