# Sintel: A Machine Learning Framework to Extract Insights from Signals

arXiv: https://arxiv.org/abs/2204.09108 · HTML: https://mlsanity.com/paper/2204.09108/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 19 Apr 2022.

## Builds on (most cited first, 17 of 17)

- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data](https://mlsanity.com/paper/1811.08055/) (2018)
- [Time-Series Anomaly Detection Service at Microsoft](https://mlsanity.com/paper/1906.03821/) (2019)
- [Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark](https://mlsanity.com/paper/1510.03336/) (2015)
- [Precision and Recall for Time Series](https://mlsanity.com/paper/1803.03639/) (2018)
- [An Evaluation of Change Point Detection Algorithms](https://mlsanity.com/paper/2003.06222/) (2020)
- [Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series](https://mlsanity.com/paper/2010.05073/) (2020)
- [RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks](https://mlsanity.com/paper/2002.09545/) (2020)
- [TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks](https://mlsanity.com/paper/2009.07769/) (2020)
- [TODS: An Automated Time Series Outlier Detection System](https://mlsanity.com/paper/2009.09822/) (2020)
- [Concept Drift Detection for Streaming Data](https://mlsanity.com/paper/1504.01044/) (2015)
- [The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development](https://mlsanity.com/paper/1905.08942/) (2019)
- [MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series](https://mlsanity.com/paper/2112.05734/) (2021)
- [Understanding Concept Drift](https://mlsanity.com/paper/1704.00362/) (2017)
- [Cardea: An Open Automated Machine Learning Framework for Electronic Health Records](https://mlsanity.com/paper/2010.00509/) (2020)
- [Building an Automated and Self-Aware Anomaly Detection System](https://mlsanity.com/paper/2011.05047/) (2020)

## Built on by (most cited first, 16 of 16)

- [Streaming 4D Visual Geometry Transformer](https://mlsanity.com/paper/2507.11539/) (2025)
- [Deep Isolation Forest for Anomaly Detection](https://mlsanity.com/paper/2206.06602/) (2022)
- [SpatialVID: A Large-Scale Video Dataset with Spatial Annotations](https://mlsanity.com/paper/2509.09676/) (2025)
- [ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection](https://mlsanity.com/paper/2307.00754/) (2023)
- [Large language models can be zero-shot anomaly detectors for time series?](https://mlsanity.com/paper/2405.14755/) (2024)
- [AER: Auto-Encoder with Regression for Time Series Anomaly Detection](https://mlsanity.com/paper/2212.13558/) (2022)
- [mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale](https://mlsanity.com/paper/2506.21550/) (2025)
- [DeepVATS: Deep Visual Analytics for Time Series](https://mlsanity.com/paper/2302.03858/) (2023)
- [A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series](https://mlsanity.com/paper/2405.03234/) (2024)
- [M2AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholding](https://mlsanity.com/paper/2504.15225/) (2025)
- [Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing](https://mlsanity.com/paper/2309.11427/) (2023)
- [OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User](https://mlsanity.com/paper/2310.17748/) (2023)
- [Think Locally, Refine Globally for Memory-Efficient 3D Reconstruction](https://mlsanity.com/paper/2609.21437/) (2026)
- [Timeseria: an object-oriented time series processing library](https://mlsanity.com/paper/2410.09567/) (2024)
- [Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains](https://mlsanity.com/paper/2503.23060/) (2025)
- [Unsupervised Discovery of Long-Term Spatiotemporal Periodic Workflows in Human Activities](https://mlsanity.com/paper/2511.14945/) (2025)

## Abstract (arXiv)

The detection of anomalies in time series data is a critical task with many monitoring applications. Existing systems often fail to encompass an end-to-end detection process, to facilitate comparative analysis of various anomaly detection methods, or to incorporate human knowledge to refine output. This precludes current methods from being used in real-world settings by practitioners who are not ML experts. In this paper, we introduce Sintel, a machine learning framework for end-to-end time series tasks such as anomaly detection. The framework uses state-of-the-art approaches to support all steps of the anomaly detection process. Sintel logs the entire anomaly detection journey, providing detailed documentation of anomalies over time. It enables users to analyze signals, compare methods, and investigate anomalies through an interactive visualization tool, where they can annotate, modify, create, and remove events. Using these annotations, the framework leverages human knowledge to improve the anomaly detection pipeline. We demonstrate the usability, efficiency, and effectiveness of Sintel through a series of experiments on three public time series datasets, as well as one real-world use case involving spacecraft experts tasked with anomaly analysis tasks. Sintel's framework, code, and datasets are open-sourced at https://github.com/sintel-dev/.