Orion
Detects unsupervised time series anomalies using verified machine learning pipelines and deep learning models.
Automated machine learning pipelines to detect, score, and evaluate rare patterns in time series data.
Orion is an open-source Python library developed by MIT's Data to AI Lab for unsupervised time series anomaly detection. It provides pre-built machine learning pipelines like AER and TadGAN to flag unusual data points in sequential signals. Quantitative analysts, risk engineers, and financial data scientists should use it to detect anomalous trading activity, metric spikes, or hardware signal failures.
What it is
The library runs automated machine learning pipelines that train on historical time series signals and return Pandas DataFrames containing anomaly start times, end times, and severity scores. It includes primitives such as AER, TadGAN, and LSTM Autoencoders that benchmark higher than standard ARIMA baseline models. You can fit pipeline configurations onto signal data and perform detection on incoming streams using simple Python calls.
Who it's for
It suits data scientists, quantitative researchers, and financial engineers who work with Python, Pandas, and deep learning workflows. It does not suit non-technical analysts seeking an out-of-the-box graphical interface or non-programmers needing a pre-built web application. Teams without Python development capabilities will not be able to use raw library primitives directly.
Key features
Verified ML Pipelines
Includes pre-configured primitives such as AER, TadGAN, LSTM Autoencoders, and VAEs tuned for sequential signal data.
Standardized Fit and Detect API
Uses a uniform object-oriented interface to train models on historical signals and output predicted anomaly windows.
Built-in Benchmarking Framework
Compares pipeline detection accuracy against baseline statistical models like ARIMA across multiple ground-truth datasets.
Pandas Data Integration
Ingests time series data frames directly and returns detected anomalies formatted as structured Pandas DataFrames.
Up and running in 3 steps
Our honest verdict
What's great
- Outperforms traditional ARIMA baselines on standard benchmark datasets using models like AER and TadGAN.
- Simplifies pipeline execution by wrapping complex deep learning algorithms into standard Python objects.
- Returns precise time windows alongside quantitative anomaly severity scores in DataFrame format.
- Backed by ongoing research from MIT's Data to AI Lab.
Where it falls short
- Currently in pre-alpha development status, which can lead to API changes or dependency warnings.
- Deep learning primitives like GANs and Autoencoders require GPU resources and compute budgets to train effectively.
- Lacks a built-in graphical dashboard, requiring custom Python code for monitoring and visual alert workflows.
- Requires domain expertise to select pipelines and tune hyperparameters for complex financial signals.
Pricing & license
Orion is completely free and open source under the MIT License. There are no paid tiers or enterprise upgrades offered by the maintainers. Your costs depend entirely on the server and GPU infrastructure required to train and run your models.
Open remote AI-training roles
874 open remote AI-training roles, updated November 15, 2026; most pay $20–$170 an hour. Zekai lists them free; the application goes to the employer’s platform.
Alternatives
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FAQ
What data format does Orion expect?
Orion expects time series data in tabular format with `timestamp` and `value` columns.
Which pipeline performs best for time series anomaly detection?
According to the project benchmark leaderboard, the Auto-Encoder with Regression (AER) pipeline achieves the top win rate against ARIMA baselines.
Is GPU hardware required to run Orion?
While light statistical primitives run on CPU, neural pipelines like TadGAN and AER require GPU compute for practical training times on large signals.
