# QABBA: Symbolic Time-Series Compression via Integer-Quantized Aggregation

arXiv: https://arxiv.org/abs/2411.15209 · HTML: https://mlsanity.com/paper/2411.15209/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 72 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 20 of 20)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [The UEA multivariate time series classification archive, 2018](https://mlsanity.com/paper/1811.00075/) (2018)
- [A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification](https://mlsanity.com/paper/2205.07877/) (2022)
- [Bit-pragmatic Deep Neural Network Computing](https://mlsanity.com/paper/1610.06920/) (2016)
- [Time Series Forecasting Using LSTM Networks: A Symbolic Approach](https://mlsanity.com/paper/2003.05672/) (2020)
- [Time series compression: a survey](https://mlsanity.com/paper/2101.08784/) (2021)
- [ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series](https://mlsanity.com/paper/2003.12469/) (2020)
- [Time Series Management Systems: A Survey](https://mlsanity.com/paper/1710.01077/) (2017)
- [SAX Navigator: Time Series Exploration through Hierarchical Clustering](https://mlsanity.com/paper/1908.05505/) (2019)
- [An efficient aggregation method for the symbolic representation of temporal data](https://mlsanity.com/paper/2201.05697/) (2022)
- [Semantic Discord: Finding Unusual Local Patterns for Time Series](https://mlsanity.com/paper/2001.11842/) (2020)
- [CNAK : Cluster Number Assisted K-means](https://mlsanity.com/paper/1911.08871/) (2019)
- [Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases](https://mlsanity.com/paper/1509.07823/) (2015)
- [Parallel Two-Stage Approach for Joint Symbolic Approximation of Time Series](https://mlsanity.com/paper/2401.00109/) (2023)
- [Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly Detection](https://mlsanity.com/paper/2105.09592/) (2021)

## Abstract (arXiv)

The expansion of time-series data from sensors and monitoring systems has made compact representations increasingly important. Such representations should retain signal structure while cutting storage, transmission and computation costs. Adaptive Brownian Bridge-based Aggregation (ABBA) addresses this need by converting long numerical series into short symbolic sequences, but reductions in parameter storage and computational precision remain desirable. We propose Quantized ABBA (QABBA), a quantized version of ABBA. By quantizing the symbolic centers, QABBA reduces the parameter footprint and enables integer arithmetic while maintaining high reconstruction quality. We establish several error bounds for the additional approximation introduced by quantization: a dimension-free bound on the excess error of each segment, a time-domain reconstruction-error bound, a stability condition for symbolic assignment, and a rule for allocating bits between segment lengths and increments. The resulting symbolic strings can be passed directly to a pretrained large language model (LLM) without any extra time-series embedding layer. Experiments on the Monash regression archive, UCR Time Series Classification Archive, and UEA Multivariate Time Series Classification Archive demonstrate a practical trade-off among storage, reconstruction accuracy and downstream predictive performance. QABBA therefore provides an error-controlled, low-precision symbolic representation for time-series compression and LLM-based analysis.