# Time series compression: a survey

arXiv: https://arxiv.org/abs/2101.08784 · HTML: https://mlsanity.com/paper/2101.08784/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2021.

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

- [A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models](https://mlsanity.com/paper/2509.11575/) (2025)
- [Unsupervised Representation Learning for Time Series: A Review](https://mlsanity.com/paper/2308.01578/) (2023)
- [Dataset Condensation for Time Series Classification via Dual Domain Matching](https://mlsanity.com/paper/2403.07245/) (2024)
- [Lossless Compression for LLM Tensor Incremental Snapshots](https://mlsanity.com/paper/2505.09810/) (2025)
- [Learned Compression of Nonlinear Time Series With Random Access](https://mlsanity.com/paper/2412.16266/) (2024)
- [Addressing Challenges in Time Series Forecasting: A Comprehensive Comparison of Machine Learning Techniques](https://mlsanity.com/paper/2503.20148/) (2025)
- [Beyond All-to-All: Causal-Aligned Transformer with Dynamic Structure Learning for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2505.16308/) (2025)
- [In-Context Source and Channel Coding](https://mlsanity.com/paper/2601.10267/) (2026)
- [Image Transformation for IoT Time-Series Data: A Review](https://mlsanity.com/paper/2311.12742/) (2023)
- [Deep Dict: Deep Learning-based Lossy Time Series Compressor for IoT Data](https://mlsanity.com/paper/2401.10396/) (2024)
- [QABBA: Symbolic Time-Series Compression via Integer-Quantized Aggregation](https://mlsanity.com/paper/2411.15209/) (2024)
- [Detection of anomalies in cow activity using wavelet transform based features](https://mlsanity.com/paper/2502.21051/) (2025)
- [Piecewise Linear Approximation in Learned Index Structures: Theoretical and Empirical Analysis](https://mlsanity.com/paper/2506.20139/) (2025)
- [EdgeCodec: Onboard Lightweight High Fidelity Neural Compressor with Residual Vector Quantization](https://mlsanity.com/paper/2507.06040/) (2025)
- [Lossless Compression: A New Benchmark for Time Series Model Evaluation](https://mlsanity.com/paper/2509.21002/) (2025)
- [Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model](https://mlsanity.com/paper/2609.06008/) (2026)

## Abstract (arXiv)

The presence of smart objects is increasingly widespread and their ecosystem, also known as Internet of Things, is relevant in many different application scenarios. The huge amount of temporally annotated data produced by these smart devices demand for efficient techniques for transfer and storage of time series data. Compression techniques play an important role toward this goal and, despite the fact that standard compression methods could be used with some benefit, there exist several ones that specifically address the case of time series by exploiting their peculiarities to achieve a more effective compression and a more accurate decompression in the case of lossy compression techniques. This paper provides a state-of-the-art survey of the principal time series compression techniques, proposing a taxonomy to classify them considering their overall approach and their characteristics. Furthermore, we analyze the performances of the selected algorithms by discussing and comparing the experimental results that where provided in the original articles. The goal of this paper is to provide a comprehensive and homogeneous reconstruction of the state-of-the-art which is currently fragmented across many papers that use different notations and where the proposed methods are not organized according to a classification.