# Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series Summarization

arXiv: https://arxiv.org/abs/2308.13722 · HTML: https://mlsanity.com/paper/2308.13722/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 50 references that are in the map.
- First posted on arXiv 26 Aug 2023.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables](https://mlsanity.com/paper/1611.00712/) (2016)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [k-Sparse Autoencoders](https://mlsanity.com/paper/1312.5663/) (2013)
- [A* Sampling](https://mlsanity.com/paper/1411.0030/) (2014)
- [AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark](https://mlsanity.com/paper/1807.03418/) (2018)
- [SOM-VAE: Interpretable Discrete Representation Learning on Time Series](https://mlsanity.com/paper/1806.02199/) (2018)
- [An efficient aggregation method for the symbolic representation of temporal data](https://mlsanity.com/paper/2201.05697/) (2022)
- [Interactive Summarization and Exploration of Top Aggregate Query Answers](https://mlsanity.com/paper/1807.11634/) (2018)
- [LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos](https://mlsanity.com/paper/1806.09963/) (2018)
- [Integrated Time Series Summarization and Prediction Algorithm and its Application to COVID-19 Data Mining](https://mlsanity.com/paper/2005.00592/) (2020)
- [Guided Exploration of Data Summaries](https://mlsanity.com/paper/2205.13956/) (2022)

## Abstract (arXiv)

Data summarization is the process of generating interpretable and representative subsets from a dataset. Existing time series summarization approaches often search for recurring subsequences using a set of manually devised similarity functions to summarize the data. However, such approaches are fraught with limitations stemming from an exhaustive search coupled with a heuristic definition of series similarity. Such approaches affect the diversity and comprehensiveness of the generated data summaries. To mitigate these limitations, we introduce an approach to time series summarization, called Time-to-Pattern (T2P), which aims to find a set of diverse patterns that together encode the most salient information, following the notion of minimum description length. T2P is implemented as a deep generative model that learns informative embeddings of the discrete time series on a latent space specifically designed to be interpretable. Our synthetic and real-world experiments reveal that T2P discovers informative patterns, even in noisy and complex settings. Furthermore, our results also showcase the improved performance of T2P over previous work in pattern diversity and processing scalability, which conclusively demonstrate the algorithm's effectiveness for time series summarization.