# Deep Dict: Deep Learning-based Lossy Time Series Compressor for IoT Data

arXiv: https://arxiv.org/abs/2401.10396 · HTML: https://mlsanity.com/paper/2401.10396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Time series compression: a survey](https://mlsanity.com/paper/2101.08784/) (2021)
- [DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression](https://mlsanity.com/paper/1901.09124/) (2019)
- [Time Series Management Systems: A Survey](https://mlsanity.com/paper/1710.01077/) (2017)
- [LFZip: Lossy compression of multivariate floating-point time series data via improved prediction](https://mlsanity.com/paper/1911.00208/) (2019)
- [Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal Analysis](https://mlsanity.com/paper/1807.03710/) (2018)

## Abstract (arXiv)

We propose Deep Dict, a deep learning-based lossy time series compressor designed to achieve a high compression ratio while maintaining decompression error within a predefined range. Deep Dict incorporates two essential components: the Bernoulli transformer autoencoder (BTAE) and a distortion constraint. BTAE extracts Bernoulli representations from time series data, reducing the size of the representations compared to conventional autoencoders. The distortion constraint limits the prediction error of BTAE to the desired range. Moreover, in order to address the limitations of common regression losses such as L1/L2, we introduce a novel loss function called quantized entropy loss (QEL). QEL takes into account the specific characteristics of the problem, enhancing robustness to outliers and alleviating optimization challenges. Our evaluation of Deep Dict across ten diverse time series datasets from various domains reveals that Deep Dict outperforms state-of-the-art lossy compressors in terms of compression ratio by a significant margin by up to 53.66%.