# Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor Streams

arXiv: https://arxiv.org/abs/2112.03360 · HTML: https://mlsanity.com/paper/2112.03360/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 95 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [A Recurrent Latent Variable Model for Sequential Data](https://mlsanity.com/paper/1506.02216/) (2015)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [On the Computational Efficiency of Training Neural Networks](https://mlsanity.com/paper/1410.1141/) (2014)
- [Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence](https://mlsanity.com/paper/2007.13018/) (2020)
- [Kernel Change-point Detection with Auxiliary Deep Generative Models](https://mlsanity.com/paper/1901.06077/) (2019)
- [Scan B-Statistic for Kernel Change-Point Detection](https://mlsanity.com/paper/1507.01279/) (2015)
- [Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation](https://mlsanity.com/paper/2008.09524/) (2020)
- [Time Series Segmentation through Automatic Feature Learning](https://mlsanity.com/paper/1801.05394/) (2018)
- [Characteristic Kernels and Infinitely Divisible Distributions](https://mlsanity.com/paper/1403.7304/) (2014)
- [Nonparametric Detection of Anomalous Data Streams](https://mlsanity.com/paper/1405.2294/) (2014)
- [Individualized Time-Series Segmentation for Mining Mobile Phone User Behavior](https://mlsanity.com/paper/1811.09577/) (2018)
- [Correlation-aware Unsupervised Change-point Detection via Graph Neural Networks](https://mlsanity.com/paper/2004.11934/) (2020)

## Abstract (arXiv)

Timeseries partitioning is an essential step in most machine-learning driven, sensor-based IoT applications. This paper introduces a sample-efficient, robust, time-series segmentation model and algorithm. We show that by learning a representation specifically with the segmentation objective based on maximum mean discrepancy (MMD), our algorithm can robustly detect time-series events across different applications. Our loss function allows us to infer whether consecutive sequences of samples are drawn from the same distribution (null hypothesis) and determines the change-point between pairs that reject the null hypothesis (i.e., come from different distributions). We demonstrate its applicability in a real-world IoT deployment for ambient-sensing based activity recognition. Moreover, while many works on change-point detection exist in the literature, our model is significantly simpler and can be fully trained in 9-93 seconds on average with little variation in hyperparameters for data across different applications. We empirically evaluate Cadence on four popular change point detection (CPD) datasets where Cadence matches or outperforms existing CPD techniques.