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

Authors: Alnegheimish, Sarah and Liu, Dongyu and Sala, Carles and Berti-Equille, Laure and Veeramachaneni, Kalyan

Abstract: 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, and through a real-world use case with spacecraft experts. Sintel’s framework, code, and datasets are open-sourced at https://github.com/sintel-dev/

### Citation (Chicago Manual of Style 17th edition)

Alnegheimish, Sarah, Dongyu Liu, Carles Sala, Laure Berti-Equille, and Kalyan Veeramachaneni. 2022. “Sintel: A Machine Learning Framework to Extract Insights from Signals.” In Proceedings of the 2022 International Conference on Management of Data, 1855–65. SIGMOD ’22. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3514221.3517910.

### BibTeX

@inproceedings{10.1145/3514221.3517910,
author = {Alnegheimish, Sarah and Liu, Dongyu and Sala, Carles and Berti-Equille, Laure and Veeramachaneni, Kalyan},
title = {Sintel: A Machine Learning Framework to Extract Insights from Signals},
year = {2022},
isbn = {9781450392495},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3514221.3517910},
doi = {10.1145/3514221.3517910},
booktitle = {Proceedings of the 2022 International Conference on Management of Data},
pages = {1855–1865},
numpages = {11},
keywords = {machine learning framework, anomaly detection, time series data, data science pipeline, human-in-the-loop AI},